Search Results (3,709)

Ground-based three-dimensional motion testing of space manipulators typically relies on active suspension-based gravity compensation systems. The design of such systems faces two fundamental challenges: first, how multiple suspension winch units can precisely track the dynamic trajectories of the corresponding suspension interfaces on the manipulator; and second, how to achieve optimal collision avoidance among the suspension mechanisms themselves during the tracking process. To address these challenges, this paper presents a multi-point suspension system endowed with kinematic redundancy for the trajectory tracking task, thereby ensuring precise tracking of the manipulator’s complex three-dimensional motions. The key innovation of this work lies in formulating the internal collision avoidance constraints as safety distance functions and integrating them into the system states. These are then combined with the trajectory-tracking states to construct a unified state-extended system model that exhibits typical underactuated characteristics. For this model, and under the concurrent influence of external disturbances from both the manipulator’s motion and the proximity to collision boundaries, a dedicated Model Predictive Controller (MPC) is designed. The results demonstrate that the proposed controller can generate an optimal coordinated collision-avoidance motion plan for the suspension winch units while maintaining precise trajectory tracking, thereby effectively solving the coordinated motion-planning problem for such complex underactuated systems. The proposed MPC achieves maximum tracking errors of 0.64 mm (X) and 0.13 mm (Z)—substantially lower than the 1.3 mm and 1.9 mm results listed in the comparative scheme—while delivering optimal collision avoidance, which is only suboptimally realized in the baseline. Full article

To address the task-grouping problem for air–ground integrated Unmanned Aerial Vehicle (UAV) swarm missions in three-dimensional (3D) environments, this study proposes a data-preprocessing and hybrid initialization clustering method based on 3D spatial features. A dual-modal prototype meta-heuristic optimization model, Dual-Prototype Metaheuristic K-Means (DPM-Kmeans), is constructed accordingly. First, to overcome spatial information loss in high-dimensional task allocation, a 3D spatial task data preprocessing technique and a hybrid initialization strategy based on the golden spiral distribution are designed. This ensures the diversity and environmental adaptability of the initial solutions. Second, a dual-modal prototype optimization framework incorporating row prototypes (local refinement) and column prototypes (global combination) was constructed using meta-heuristics and clustering algorithms. The prototype-driven replacement update mechanism simultaneously performs global and local search, balancing the algorithm’s exploration and exploitation capabilities while expanding the solution space. This effectively addresses premature convergence issues in complex search spaces. Simultaneously, a collaborative multi-constraint, dynamically weighted optimization model was constructed, incorporating task requirements and flight distance constraints to ensure that the grouping scheme approximates the global optimum. Simulation results demonstrate that compared to traditional K-means and mainstream meta-heuristic optimization algorithms, DPM-Kmeans achieves an overall improvement of 2–10% in Sum of Squared Errors (SSE), Silhouette Coefficient (SC), and Davies–Bouldin Index (DB) metrics. It exhibits superior convergence speed and solution quality, proving the method’s excellent scalability and robustness in multi-constraint, large-scale 3D scenarios. Full article

Accurate localization is a persistent challenge for Mixed Reality (MR) applications in the construction industry, where reliable alignment between digital building models and physical environments is critical. Commercial MR devices such as the Microsoft HoloLens rely on Visual-Inertial Simultaneous Localization and Mapping (VISLAM) for pose estimation, but accumulated drift over extended trajectories and visually ambiguous indoor spaces often reduces localization accuracy. This paper presents a complementary localization refinement methodology that integrates HoloLens spatial tracking with image style transfer and geometry-based pose estimation for Building Information Modeling (BIM)-aligned MR visualization. Image style transfer is used to reduce appearance discrepancies between real-world images and synthetic BIM renderings, improving feature correspondence for geometric alignment. Pose refinement is then applied using feature matching and Perspective-n-Point (PnP) estimation to mitigate accumulated drift when sufficient visual evidence is available. The method is evaluated using 1408 image pairs captured along an indoor trajectory, demonstrating improved BIM alignment, significantly reducing accumulated drift to 1–2 pixels. The proposed approach supports more reliable MR visualization for construction-related tasks such as inspection, coordination, and spatial decision-making. Full article

Terahertz (THz) sub-millimeter wave imaging offers unique capabilities for stand-off biometrics through concealment, yet it suffers from severe sparsity, low resolution, and high noise. To address these limitations, we introduce a novel unified Multi-Task Learning (MTL) network centered on a custom shared U-Net-like THz data encoder. This network is designed to simultaneously solve three distinct critical tasks on concealed THz facial data, given a limited dataset of approximately 1400 THz facial images of 20 different identities. The tasks include concealed face verification, facial posture classification, and a generative reconstruction of unconcealed faces from concealed ones. While providing highly successful MTL results as a standalone solution on the very challenging dataset, we further studied the expansion of this architecture via a cross-modal teacher-student approach. During training, a privileged visible-spectrum teacher fuses limited visible features with THz data to guide the THz-only student. This distillation process yields a student network that relies solely on THz inputs at inference. The cross-modal trained student achieves better latent space in terms of inter-class separability compared to the single-modality baseline, but with reduced intra-class compactness, while maintaining a similar success in the task performances. Both THz-only and distilled models preserve high unconcealed face generative fidelity. Full article

Industry 5.0 emphasizes human-centered integration of artificial intelligence in industrial contexts, yet successful adoption depends critically on workforce perception and acceptance. This research develops and validates a machine learning framework for predicting AI-related perceptions and expected impacts in the construction industry under small sample constraints typical of specialized industrial surveys. Specifically, the study aims to develop and empirically validate a predictive AI decision support model that estimates the expected impact of AI adoption in the construction sector based on digital competencies, ICT utilization, AI training and experience, and AI usage at both individual and organizational levels, operationalized through a composite AI Impact Index and two process-oriented outcomes (perceived task automation and perceived cost reduction). Using a dataset of 51 survey responses from Slovak construction professionals collected in 2025, we implement a methodologically rigorous approach specifically designed for limited-data regimes. The framework encompasses ordinal target simplification from five to three classes, dimensionality reduction through theoretically grounded composite indices reducing features from 15 to 7, exclusive deployment of low variance regularized models, and leave-one-out cross-validation for unbiased performance estimation. The optimal model (Lasso regression with recursive feature elimination) predicts cost reduction perception with R² = 0.501, MAE = 0.551, and RMSE = 0.709, while six classification targets achieve weighted F1 = 0.681, representing statistically optimal performance given sample constraints and perception measurement variability. Comparative evaluation confirms regularized models outperform high variance alternatives: random forest (R² = 0.412) and gradient boosting (R² = 0.292) exhibit substantially lower generalization performance, empirically validating the bias-variance trade-off rationale. Key methodological contributions include explicit bias-variance optimization preventing overfitting, feature selection via RFE reducing input space to six predictors (personal AI usage, AI impact on budgeting, ICT utilization, AI training, company size, and age), and demonstration that principled statistical approaches achieve meaningful predictions without requiring large-scale datasets or complex architectures. The framework provides a replicable blueprint for perception and impact prediction in data-constrained Industry 5.0 contexts, enabling targeted interventions, including customized training programs, strategic communication prioritization, and resource allocation for change management initiatives aligned with predicted adoption patterns. Full article

As large language models (LLMs) are increasingly used as digital respondents and generative agents in computational social science, prior work has primarily focused on the fidelity of their expressed opinions, often overlooking a fundamental question: the behavioral stability of outputs across repeated runs of the same model when the persona specification and task conditions remain unchanged. This paper proposes a behavioral stability evaluation framework for role-playing tasks, using the Political Compass questionnaire as the testbed. The questionnaire maps responses onto a two-dimensional coordinate system defined by an economic axis and a social axis, enabling political orientations to be directly quantified and compared in a continuous space. To ground the simulation in realistic user behaviors, we construct personas from publicly available social media texts and stratify them based on Political Signal Clarity. Across three LLMs, we compare repeated questionnaire completions under different decoding temperatures and prompting strategies. We characterize it along two complementary dimensions: dispersion of the resulting two-dimensional coordinates across runs, measured by an Overall Stability Score (OSS), and dispersion of per-item choices across runs, quantified by response entropy. We further use linear mixed-effects models to account for persona-level heterogeneity and to estimate the effects of key factors on stability. Our results show that coordinate drift and item-level dispersion do not always move in tandem. Increasing temperature typically amplifies variability, although models differ in their sensitivity. Contrary to its success in reasoning tasks, Chain-of-Thought (CoT) prompting failed to enhance stability in this value-laden context. Instead, it frequently amplified coordinate drift by introducing stochasticity into intermediate reasoning steps. Results show that LLMs exhibit greater behavioral stability when role-playing personas with clearer political signals. These findings suggest that stability should be treated as a pre-study benchmark before deploying LLM-based role-playing simulations, and that key generation settings and stability statistics should be reported alongside substantive conclusions. Full article

Visible-infrared image fusion is crucial for applications like autonomous driving and nighttime surveillance, yet it remains challenging due to the inherent limitations of existing deep learning models. Convolutional Neural Networks (CNNs) are constrained by their local receptive fields, while Transformers suffer from quadratic computational complexity. To address these issues, this paper investigates the application of the Mamba model—a novel State Space Model (SSM) with linear-complexity global modeling and selective scanning capabilities—to the task of visible-infrared image fusion. Building upon Mamba, we propose a novel fusion framework featuring two key designs: (1) A Multi-Path Mamba (MPMamba) module that orchestrates parallel Mamba blocks with convolutional streams to extract multi-scale, modality-specific features; and (2) a Dual-path Mamba Attention Fusion (DMAF) module that explicitly decouples and processes shared and complementary features via dual Mamba paths, followed by dynamic calibration with a Convolutional Block Attention Module (CBAM). Extensive experiments on the MSRS benchmark demonstrate that our framework achieves state-of-the-art performance, outperforming strong baselines such as U2Fusion and SwinFusion across key metrics including Information Entropy (EN), Spatial Frequency (SF), Mutual Information (MI), and edge-based fusion quality (Qabf). Visual results confirm its ability to produce fused images that saliently preserve thermal targets while retaining rich texture details. Full article

Although Least Squares Support Vector Regression (LSSVR) reduces the hyperparameter space to two, it sacrifices sparsity, causing all training samples to become support vectors and increasing storage costs. In contrast, standard Support Vector Regression (SVR) preserves sparsity but requires tuning three highly coupled hyperparameters, leading to higher computational burden. To address these limitations, this paper proposes a sparse ${\mathcal{L}}_{\infty }$-norm regularized least squares SVR framework that incorporates the infinity norm of approximation errors into both the objective function and inequality constraints. The resulting optimization problem minimizes model complexity while controlling the maximum prediction deviation through a single slack variable, thereby transforming the conventional three-hyperparameter SVR tuning task into a two-parameter problem involving only the regularization coefficient and kernel width. This formulation restores sparsity by enabling a compact support vector set, while preserving the stability and convexity advantages of LSSVR. Experiments on both static and dynamic datasets demonstrate that the proposed method consistently achieves higher predictive accuracy and improved robustness compared with standard SVR and LSSVR. These results indicate that the proposed ${\mathcal{L}}_{\infty }$-norm regularized framework offers a mathematically principled and computationally efficient alternative for sparse, robust, and scalable regression modeling. Full article

Background/Objectives: People with advanced dementia experience difficulties in navigating, while wayfinding is essential for a level of autonomy. A properly designed building has the strength to facilitate this target group in wayfinding. While understanding their wayfinding needs and experiences, and the spatial characteristics (both facilitating and confusing) during the wayfinding process is crucial, this knowledge is still limited. This study mapped challenges that people with advanced dementia encounter on a route to an irregular destination in their familiar nursing home environment, specifically addressing confusing spatial characteristics. Methods: An observational study design with a mixed-method approach was applied. First, a navigation task was conducted to identify places of confusion on the way to the destination (n = 15 participants in four nursing homes). Affective states were captured by observations combined with biometric measurements. Second, both manual and space syntax floorplan analysis techniques were used to identify the spatial characteristics of potentially stressful spaces in nursing homes. Results: Nine participants reached the destination. The most observed wayfinding behaviors were looking at various things and stops on the route, and they were often accompanied by verbal navigational cues. Furthermore, most participants experienced some signs of stress or concentration. In total, eighteen confusing places in the nursing homes were identified. Conclusions: Regarding spatial characteristics supporting or hindering wayfinding skills for people with advanced dementia in nursing homes, this study confirmed some of the findings in the existing research (e.g., minimizing shifting directions for supporting wayfinding), contradicted the existing research (e.g., confusion arose at places with high visibility values), and added new findings (e.g., significantly widening corridors may be confusing). This study revealed that high-visibility areas and squares on the route confuse people with advanced dementia while wayfinding. Full article

In signal processing and information analysis, the detection and identification of anomalies present in signals constitute a critical research focus. Accurately discerning these deviations using probabilistic, statistical, and information-theoretic methods is essential for ensuring data integrity and supporting reliable downstream analysis. Outlier detection in functional data aims to identify curves or trajectories that deviate significantly from the dominant pattern—a process vital for data cleaning and the discovery of anomalous events. This task is challenging due to the intrinsic infinite dimensionality of functional data, where outliers often appear as subtle shape deformations that are difficult to detect. Moving beyond conventional approaches that discretize curves into multivariate vectors, we introduce a novel framework that projects functional data into a low-dimensional space of meaningful features. This is achieved via a tailored weighting scheme designed to preserve essential curve variations. We then incorporate the Mahalanobis distance to detect directional outlyingness under non-Gaussian assumptions through a robustified bootstrap resampling method with data-driven threshold determination. Simulation studies validated its superior performance, demonstrating higher true positive and lower false positive rates across diverse anomaly types, including magnitude, shape-isolated, shape-persistent, and mixed outliers. The practical utility of our approach was further confirmed through applications in environmental monitoring using seawater spectral data, character trajectory analysis, and population data underscoring its cross-domain versatility. Full article

Cyberbullying demonstrates notable metaphorical and contextual traits, characterized by a high-dimensional sparse semantic space and dynamic evolution. Pre-trained models utilize extensive textual data for learning and employ transformer-based word vector generation techniques to accurately capture intricate semantics and nuanced syntax in text. However, although a single pre-trained model demonstrates strong performance in contextual modeling, it still faces challenges including inadequate feature representation and limited generalization capability in classifying cyberbullying texts. This study proposes a cyberbullying detection model employing BERT-BiGRU-CNN (BBGC) to address this issue. The BBGC model initially employs BERT to produce word embeddings, subsequently inputs them into a BiGRU layer to acquire sequence features, and finally utilizes a CNN for the extraction of local features. The features derived from BERT, BiGRU, and CNN are integrated, followed by the application of the softmax function to yield the final outcome of cyberbullying detection. Experimental findings indicate that the BBGC fusion model surpasses individual pre-trained models in the task of detecting cyberbullying text. Furthermore, in comparison to hybrid neural network models utilizing RoBERTa, ALBERT, DistilBERT and other pre-trained models, the BBGC model demonstrates considerable advantages. Full article

The superposition principle in quantum mechanics enables the encoding of an entire solution space within a single quantum state. By employing quantum routines such as amplitude amplification or the Quantum Approximate Optimization Algorithm (QAOA), this solution space can be explored in a computationally efficient manner to identify optimal or near-optimal solutions. In this article, we propose quantum circuits that operate on binary data representations to address a central task in prototype-based classification and representation learning, namely the so-called winner determination, which realizes the nearest prototype principle. We investigate quantum search algorithms to identify the closest prototype during prediction, as well as quantum optimization schemes for prototype selection in the training phase. For these algorithms, we design oracles based on arithmetic circuits that leverage quantum parallelism to apply mathematical operations simultaneously to multiple inputs. Furthermore, we introduce an oracle for prototype selection, integrated into a learning routine, which obviates the need for formulating the task as a binary optimization problem and thereby reduces the number of required auxiliary variables. All proposed oracles are implemented using the Python 3-based quantum machine learning framework PennyLane and empirically validated on synthetic benchmark datasets. Full article

An online off-policy asynchronous real-time model reference tracking control (OOART-MRTC) algorithm is proposed and validated for unmanned aerial vehicles (UAVs) characterized by faulty actuation and parametric uncertainty. The optimal control problem is posed based on approximate dynamic programming (ADP) and reinforcement learning (RL) theory, using a virtual state-space representation constructed exclusively on input–output true system data, which exploits the observability theory. OOART-MRTC learns control by interacting with the system, starting from an initial stabilizing controller derived from an approximate uncertain model. Learning convergence and stability under the proposed adaptive behavior are analyzed. Since the learning iterations cannot update within a sampling period, an asynchronous mechanism is proposed for updating the controller parameters, leveraging real-time control and multi-tasking. The complexity associated with the resulting high-dimensional system is solved by efficient linear parameterization and validated on a realistic case study where three coupled double integrators describe the UAV attitude control. Full article

As UAV popularity soars, so does the mission planning associated with it. Classical planning approaches suffer from the triple problems of decoupling of task assignment and path planning, poor real-time and scalability, and limited adaptability. Aiming at these challenges, this paper proposes a multi-UAV real-time collaborative mission planning method based on UAV states. First, the employed Dubins path accurately represents the distance between tasks and satisfies curvature constraints without smoothing, thus achieving a coupled solution for task assignment and path planning. Then, a series of acceleration techniques are applied to guarantee the real-time performance of the method, including task clustering to reduce the decision space, allocation strategies with fewer iterations, and efficient distance cost calculation methods. To enhance robustness and adaptability, real-time assignment of new tasks and task reassignment due to the reduction of available UAVs are appropriately handled. Finally, simulations highlight that the proposed method only increases the path length by 9.57% compared to benchmark method, while achieving a 4–5 orders-of-magnitude improvement in planning speed, with a single mission planning of about 0.0003 s. Moreover, it easily scales to large-scale scenarios (0.0029 s, with 1000 UAVs and 25,000 tasks). Full article

Deep learning has profoundly transformed remote sensing, yet prevailing architectures like Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) remain constrained by critical trade-offs: CNNs suffer from limited receptive fields, while ViTs grapple with quadratic computational complexity, hindering their scalability for high-resolution remote sensing data. State Space Models (SSMs), particularly the recently proposed Mamba architecture, have emerged as a paradigm-shifting solution, combining linear computational scaling with global context modeling. This survey presents a comprehensive review of Mamba-based methodologies in remote sensing, systematically analyzing about 120 Mamba-based remote sensing studies to construct a holistic taxonomy of innovations and applications. Our contributions are structured across five dimensions: (i) foundational principles of Vision Mamba architectures, (ii) micro-architectural advancements such as adaptive scan strategies and hybrid SSM formulations, (iii) macro-architectural integrations, including CNN–Transformer–Mamba hybrids and frequency-domain adaptations, (iv) rigorous benchmarking against state-of-the-art methods in multiple application tasks, such as object detection, semantic segmentation, change detection, etc. and (v) critical analysis of unresolved challenges with actionable future directions. By bridging the gap between SSM theory and remote sensing practice, this survey establishes Mamba as a transformative framework for remote sensing analysis. To our knowledge, this paper is the first systematic review of Mamba architectures in remote sensing. Our work provides a structured foundation for advancing research in remote sensing systems through SSM-based methods. We curate an open-source GitHub repository to foster community-driven advancements. Full article