Search Results (206)

This study explores the feasibility of generating a globular microstructure in situ during the thixotropic 3D printing of the EN AW-4043 alloy, starting from a conventional cold-rolled wire. Thermodynamic simulations using Thermo-Calc software were first conducted to identify the semi-solid processing window of the alloy, based on the evolution of liquid and solid fractions as a function of temperature. Guided by these results, thermal treatments were performed on cold-rolled wires to promote the formation of a globular microstructure. A laboratory-scale printing head prototype was then designed and built to test continuous heating and deposition conditions representative of a thixotropic additive manufacturing process. The results showed that a globular microstructure could be achieved in the cold-rolled EN AW-4043 wires by heating them at 590 °C for 5 min in a static muffle furnace. A similar effect was observed when continuously heating the wire while it flowed through the heated printing head. Preliminary deposition tests confirmed the viability of this approach and demonstrated that thixotropic 3D printing of EN AW-4043 alloy is achievable without the need for pre-globular feedstock. Full article

The Segment Anything Model 2 (SAM2) has achieved state-of-the-art performance in pixel-level object segmentation for both static and dynamic visual content. Its streaming memory architecture maintains spatial context across video sequences, yet struggles with long-term tracking due to its static inference framework. SAM 2’s fixed temporal window approach indiscriminately retains historical frames, failing to account for frame quality or dynamic motion patterns. This leads to error propagation and tracking instability in challenging scenarios involving fast-moving objects, partial occlusions, or crowded environments. To overcome these limitations, this paper proposes SAM2Plus, a zero-shot enhancement framework that integrates Kalman filter prediction, dynamic quality thresholds, and adaptive memory management. The Kalman filter models object motion using physical constraints to predict trajectories and dynamically refine segmentation states, mitigating positional drift during occlusions or velocity changes. Dynamic thresholds, combined with multi-criteria evaluation metrics (e.g., motion coherence, appearance consistency), prioritize high-quality frames while adaptively balancing confidence scores and temporal smoothness. This reduces ambiguities among similar objects in complex scenes. SAM2Plus further employs an optimized memory system that prunes outdated or low-confidence entries and retains temporally coherent context, ensuring constant computational resources even for infinitely long videos. Extensive experiments on two video object segmentation (VOS) benchmarks demonstrate SAM2Plus’s superiority over SAM 2. It achieves an average improvement of 1.0 in J&F metrics across all 24 direct comparisons, with gains exceeding 2.3 points on SA-V and LVOS datasets for long-term tracking. The method delivers real-time performance and strong generalization without fine-tuning or additional parameters, effectively addressing occlusion recovery and viewpoint changes. By unifying motion-aware physics-based prediction with spatial segmentation, SAM2Plus bridges the gap between static and dynamic reasoning, offering a scalable solution for real-world applications such as autonomous driving and surveillance systems. Full article

Low-latency and robust beamforming are vital for sustaining signal quality and spectral efficiency in emerging high-mobility 5G and future 6G wireless networks. Conventional beam management approaches, which rely on periodic Channel State Information feedback and static codebooks, as outlined in 3GPP standards, are insufficient in rapidly varying propagation environments. In this work, we propose a Dominance-Enforced Adaptive Clustered Sliding Window Regression (DE-ACSW-R) framework for predictive beamforming in O-RAN Split 7-2x architectures. DE-ACSW-R leverages a sliding window of recent angle of arrival (AoA) estimates, applying in-window change-point detection to segment user trajectories and performing both Linear Regression (LR) and curvature-adaptive Support Vector Regression (SVR) for short-term and non-linear prediction. A confidence-weighted fusion mechanism adaptively blends LR and SVR outputs, incorporating robust outlier detection and a dominance-enforced selection regime to address strong disagreements. The Open Radio Unit (O-RU) autonomously triggers localised MUSIC scans when prediction confidence degrades, minimising unnecessary full-spectrum searches and saving delay. Simulation results demonstrate that the proposed DE-ACSW-R approach significantly enhances AoA tracking accuracy, beamforming gain, and adaptability under realistic high-mobility conditions, surpassing conventional LR/SVR baselines. This AI-native modular pipeline aligns with O-RAN architectural principles, enabling scalable and real-time beam management for next-generation wireless deployments. Full article

Dueto uncertainties in real-world production, dynamic factors have become increasingly critical in the research of distributed flexible job shop scheduling problems. Effectively responding to dynamic events can significantly enhance the adaptability and quality of scheduling solutions, thereby improving the resilience of manufacturing systems. This study addresses the dynamic distributed flexible job shop scheduling problem, which involves random job arrivals and machine breakdowns, and proposes an effective discrete improved gray wolf optimization (DIGWO) algorithm-based predictive–reactive method. The first contribution of our work lies in its dynamic scheduling strategy: a periodic- and event-driven approach is used to capture the dynamic nature of the problem, and a static scheduling window is constructed based on updated factory and workshop statuses to convert dynamic scheduling into static scheduling at each rescheduling point. Second, a mathematical model of multi-objective distributed flexible job shop scheduling (MODDFJSP) is established, optimizing makespan, tardiness, maximal factory load, and stability. The novelty of the model is that it is capable of optimizing both production efficiency and operational stability in the workshop. Third, by designing an efficacious initialization mechanism, prey search, and an external archive, the DIGWO algorithm is developed to solve conflicting objectives and search for a set of trade-off solutions. Experimental results in a simulated dynamic distributed flexible job shop demonstrate that DIGWO outperforms three well-known algorithms (NSGA-II, SPEA2, and MOEA/D). The proposed method also surpasses completely reactive scheduling approaches based on rule combinations. This study provides a reference for distributed manufacturing systems facing random job arrivals and machine breakdowns. Full article

In mobile robot path planning, the traditional A* algorithm suffers from high path redundancy and poor smoothness, while the Dynamic Window Approach (DWA) tends to deviate from the global optimal path and has low efficiency in avoiding dynamic obstacles when integrated with global path planning. To address these issues, a smoothing optimised A*-guided DWA fusion algorithm (SOA-DWA) is proposed in this paper. Firstly, the A* algorithm was improved by introducing a path smoothing strategy and path pruning mechanism, generating a globally optimal path that complied with the vehicle kinematic constraints. Secondly, three sub-functions were introduced into the evaluation function of the DWA algorithm: the distance evaluation between the reference trajectory and the global path, the path direction evaluation, and the dynamic obstacle avoidance evaluation, to enhance the real-time performance of dynamic obstacle avoidance and the consistency of the global path. The SOA-DWA algorithm ensured that the mobile robot could effectively avoid obstacles in complex environments without deviating from the global optimal path. Thirdly, experimental results show that in a static environment, the path length and turning angle of the SOA-DWA algorithm are reduced by an average of 13.3% and 16.25%, respectively, compared with the traditional algorithm. In a dynamic environment, the path length and turning angle are reduced by an average of 10.5% and 14.5% compared to the traditional DWA algorithm, respectively, significantly improving the smoothness of the path and driving safety. Compared to the existing fusion algorithm, the SOA-DWA algorithm reduces the path length by an average of 10.1%, improves planning efficiency by an average of 42%, and effectively enhances obstacle avoidance efficiency. Finally, the effectiveness of the improved algorithm proposed in this paper was further verified by mobile robot experiments. Full article

This paper addresses the limitations of the A* algorithm in underground roadway path planning, such as proximity to roadway boundaries, intersection with obstacle corners, trajectory smoothness, and timely obstacle avoidance (e.g., fallen rocks, miners, and moving equipment). To overcome these challenges, we propose an improved path planning algorithm integrating an enhanced A* method with an improved Dynamic Window Approach (DWA). First, a diagonal collision detection mechanism is implemented within the A* algorithm to effectively avoid crossing obstacle corners, thus enhancing path safety. Secondly, roadway width is incorporated into the heuristic function to guide paths toward the roadway center, improving stability and feasibility. Subsequently, based on multiple global path characteristics—including path length, average curvature, fluctuation degree, and direction change rate—an adaptive B-spline curve smoothing method generates smoother paths tailored to the robot’s kinematic requirements. Furthermore, the global path is segmented into local reference points for DWA, ensuring seamless integration of global and local path planning. To prevent local optimization traps during obstacle avoidance, a distance-based cost function is introduced into DWA’s evaluation criteria, maintaining alignment with the global path. Experimental results demonstrate that the proposed method significantly reduces node expansions by 43.79%, computation time by 16.28%, and path inflection points by 80.70%. The resultant path is smoother, centered within roadways, and capable of effectively avoiding dynamic and static obstacles, thereby ensuring the safety and efficiency of underground robotic transport operations. Full article

Industrial Control Systems (ICS) are increasingly targeted by sophisticated and evolving cyberattacks, while conventional static defense mechanisms and isolated intrusion detection models often lack the robustness required to cope with such dynamic threats. To overcome these limitations, we propose RHAD (Reinforced Heterogeneous Anomaly Detector), a resilient and adaptive anomaly detection framework specifically designed for ICS environments. RHAD combines a heterogeneous ensemble of detection models with a confidence-aware scheduling mechanism guided by reinforcement learning (RL), alongside a time-decaying sliding window voting strategy to enhance detection accuracy and temporal robustness. The proposed architecture establishes a modular collaborative framework that enables dynamic and fine-grained protection for industrial network traffic. At its core, the RL-based scheduler leverages the Proximal Policy Optimization (PPO) algorithm to dynamically assign model weights and orchestrate container-level executor replacement in real time, driven by network state observations and runtime performance feedback. We evaluate RHAD using two publicly available ICS datasets—SCADA and WDT—achieving 99.19% accuracy with an F1-score of 0.989 on SCADA, and 98.35% accuracy with an F1-score of 0.987 on WDT. These results significantly outperform state-of-the-art deep learning baselines, confirming RHAD’s robustness under class imbalance conditions. Thus, RHAD provides a promising foundation for resilient ICS security and shows strong potential for broader deployment in cyber-physical systems. Full article

Background: Electroencephalography (EEG) assists clinicians in diagnosing epileptic seizures by recording brain electrical activity. Existing models process spatiotemporal features inefficiently either through cascaded spatiotemporal architectures or static functional connectivity, limiting their ability to capture deeper spatial–temporal correlations. Objectives: To address these limitations, we propose a Graph Transformer with Spatiotemporal Attention Fusion Gate (GT-STAFG). Methods: We analyzed 18-channel EEG data sampled at 200 Hz, transformed into the frequency domain, and segmented into 30- second windows. The graph transformer exploits dynamic graph data, while STAFG leverages self-attention and gating mechanisms to capture complex interactions by augmenting graph features with both spatial and temporal information. The clinical significance of extracted features was validated using the Integrated Gradients attribution method, emphasizing the clinical relevance of the proposed model. Results: GT-STAFG achieves the highest area under the precision–recall curve (AUPRC) scores of 0.605 on the TUSZ dataset and 0.498 on the CHB-MIT dataset, surpassing baseline models and demonstrating strong cross-patient generalization on imbalanced datasets. We applied transfer learning to leverage knowledge from the TUSZ dataset when analyzing the CHB-MIT dataset, yielding an average improvement of 8.3 percentage points in AUPRC. Conclusions: Our approach has the potential to enhance patient outcomes and optimize healthcare utilization. Full article

Driven by rapid growth in the new-energy vehicle (NEV) market and advances in automation, mobile charging robots are increasingly deployed in parking facilities. In complex environments featuring both static and dynamic obstacles, conventional trajectory plans often exhibit insufficient safety margins and poor smoothness. This paper proposes a hybrid path-planning strategy that combines an improved A* algorithm with an enhanced dynamic window approach (DWA). The enhanced A* algorithm incorporates obstacle influence factors and adaptive weighting during global search, enabling proactive avoidance of obstacle-dense regions and employing segmented Bezier curves for path smoothing. In local planning, the modified DWA integrates a global guidance term and distance-dependent heading weights to mitigate issues of local minima and target loss. Simulation results indicate that the proposed method substantially improves path safety, continuity, and adaptability to complex scenarios while maintaining computational efficiency. Specifically, under high-obstacle-density conditions (e.g., a 20 × 20 grid map), the collision rate is reduced by 66.7% compared to the standard A* algorithm (from 30% to 10%), and the minimum safety distance increases to 0.5 m. Current validation is conducted in simulations; future work will involve real-robot experiments to evaluate real-time performance and robustness in practical environments. Full article

To comprehensively leverage the temporal, static, and dynamic information features of multi-channel surface electromyography (sEMG) signals for gesture recognition, considering the sensitive temporal characteristics of sEMG signals to action amplitude and muscle recruitment patterns, an sEMG-based gesture recognition algorithm is innovatively proposed using Sigimg-GADF-MTF and multi-stream convolutional neural network (MSCNN) by introducing the Sigimg, GADF, and MTF data processing methods and combining them with a multi-stream fusion strategy. Firstly, a sliding window is used to rearrange the multi-channel original sEMG signals through channels to generate a two-dimensional image (named Sigimg method). Meanwhile, each channel signal is respectively transformed into two-dimensional subimages using Gram angular difference field (GADF) and Markov transition field (MTF) methods. Then, the GADF and MTF images are obtained using a horizontal stitching method to splice these subimages, respectively. The Sigimg, GADF, and MTF images are used to construct a training and testing dataset, which is then imported into the constructed MSCNN model for experimental testing. The fully connected layer fusion method is utilized for multi-stream feature fusion, and the gesture recognition results are output. Through comparative experiments, an average accuracy of 88.4% is achieved using the Sigimg-GADF-MTF-MSCNN algorithm on the Ninapro DBl dataset, higher than most mainstream models. At the same time, the effectiveness of the proposed algorithm is fully verified through generalization testing of data obtained from the self-developed sEMG signal acquisition platform with an average accuracy of 82.4%. Full article

In southern Hunan province, a vital element of China’s architectural cultural legacy, the quality of the indoor lighting environment influences physical performance and the transmission of spatial culture. The province encounters minor environmental disparities and diminishing liveability attributed to evolving construction practices and cultural standards. The three varieties of traditional residences in Shanggantang Village are employed to assess the daylight factor (DF), illumination uniformity (U0), daylight autonomy (DA), and useful daylight illumination (UDI). We subsequently integrate field measurements with static and dynamic numerical simulations to create a multi-dimensional analytical framework termed “measured-static-dynamic”. This method enables the examination of the influence of floor plan layout on light, as well as the relationship between window size, building configuration, and natural illumination. The lighting factor (DF) of the core area of the central patio-type residence reaches 27.7% and the illumination uniformity (U0) is 0.62, but the DF of the transition area plummets to 1.6%; the composite patio type enhances the DF of the transition area to 1.2% through the alleyway-assisted lighting, which is a 24-fold improvement over the offset patio type. Parameter optimization showed that the percentage of all-natural daylighting time (DA) in the edge zone of the central patio type increased from 21.4% to 58.3% when the window height was adjusted to 90%. The results of the study provide a quantitative basis for the optimization of the light environment and low-carbon renewal of traditional residential buildings. Full article

In digital forensics, especially Windows forensics, identifying anomalous file paths is crucial when dealing with large-scale data. Traditional static embedding methods, which aggregate token-level representations, discard hierarchical and sequential relationships in file paths, leading to misclassification of anomalies. This study introduces a Transformer-based sequence modeling approach to classify anomalous file paths, addressing these limitations by preserving positional and contextual relationships. File paths from the NTFS Master File Table (MFT) were embedded using FastText to capture structural and contextual dependencies. Unlike static embeddings, the proposed method processes file paths as structured sequences to enhance anomaly detection accuracy. Extensive experiments showed that Transformer models generally outperformed traditional methods in detecting structured anomalies. The Transformer model with FastText embeddings (32 dimensions) achieved an accuracy of 0.9781 and an F1-score of 0.9782, while Random Forest with FastText embeddings (64 dimensions) achieved an accuracy of 0.9729 and an F1-score of 0.9729. These findings suggest that a hybrid anomaly detection framework combining Transformer-based models with traditional techniques could enhance robustness in forensic investigations. Future research should explore combining both methods to improve adaptability across diverse forensic scenarios. Full article

The intrinsic trade-off among sensitivity, response speed, and measurement range continues to hinder the wider adoption of flexible pressure sensors in areas such as medical diagnostics and gesture recognition. In this work, we propose a grid-structured polycaprolactone/thermoplastic-polyurethane nanofiber pressure sensor decorated with multi-walled carbon nanotubes (PCL/TPU@MWCNTs). By introducing a gradient grid membrane, the strain distribution and reconstruction of the conductive network can be modulated, thereby alleviating the conflict between sensitivity, response speed, and operating range. First, static mechanical simulations were performed to compare the mechanical responses of planar and grid membranes, confirming that the grid architecture offers superior sensitivity. Next, PCL/TPU@MWCNT nanofiber membranes were fabricated via coaxial electrospinning followed by vacuum-filtration and assembled into three-layer planar and grid piezoresistive pressure sensors. Their sensing characteristics were evaluated by simple index-finger motions and slide the mouse wheel identified. Within 0–34 kPa, the sensitivities of the planar and grid sensors reached 1.80 kPa⁻¹ and 2.24 kPa⁻¹, respectively; in the 35–75 kPa range, they were 1.03 kPa⁻¹ and 1.27 kPa⁻¹. The rise/decay times of the output signals were 10.53 ms/11.20 ms for the planar sensor and 9.17 ms/9.65 ms for the grid sensor. Both sensors successfully distinguished active index-finger bending at 0–0.5 Hz. The dynamic range of the grid sensor during the extension motion of the index finger is 105 dB and, during the scrolling mouse motion, is 55 dB, affording higher measurement stability and a broader operating window, fully meeting the requirements for high-precision hand-motion recognition. Full article

The use of renewables in heat production requires methods to overcome the issue of asynchronous heat load and energy production. The most effective method for analyzing the intricate thermal dynamics of an existing building is through transient simulation, utilizing real-world weather data. This approach offers a far more nuanced understanding than static calculations, which often fail to capture the dynamic interplay of environmental factors and building performance. Transient simulations, by their nature, model the building’s thermal behavior over time, reflecting the continuous fluctuations in temperature, solar radiation, and wind speed. Leveraging actual meteorological data enables the simulation model to faithfully capture system dynamics under realistic operational scenarios. This is crucial for evaluating the effectiveness of heating, ventilation, and air conditioning (HVAC) systems, identifying potential energy inefficiencies, and assessing the impact of various energy-saving measures. The simulation can reveal how the building’s thermal mass absorbs and releases heat, how solar gains influence indoor temperatures, and how ventilation patterns affect heat losses. In this paper, a household heating system consisting of an air source heat pump, PV, and buffer tank is simulated and analyzed. The 3D model accurately represents the building’s geometry and thermal properties. This virtual representation serves as the basis for calculating heat losses and gains, considering factors such as insulation levels, window characteristics, and building orientation. The approach is based on the calculation of building heat load based on a 3D model and EN ISO 52016-1 standard. The heat load is modeled based on air temperature and sun irradiance. The heating system is modeled in EBSILON professional 16.00 software for the calculation of transient 10 min time step heat production during the heating season. The results prove that a buffer tank with the right heat production control system can efficiently increase the auto consumption of self-produced PV electric energy, leading to a reduction in environmental effects and higher economic profitability. Full article

This paper presents a novel 3D Gaussian Splatting (3DGS)-based Simultaneous Localization and Mapping (SLAM) system that integrates Light Detection and Ranging (LiDAR) and vision data to enhance dynamic scene tracking and reconstruction. Existing 3DGS systems face challenges in sensor fusion and handling dynamic objects. To address these, we introduce a hybrid uncertainty-based 3D segmentation method that leverages uncertainty estimation and 3D object detection, effectively removing dynamic points and improving static map reconstruction. Our system also employs a sliding window-based keyframe fusion strategy that reduces computational load while maintaining accuracy. By incorporating a novel dynamic rendering loss function and pruning techniques, we suppress artifacts such as ghosting and ensure real-time operation in complex environments. Extensive experiments show that our system outperforms existing methods in dynamic object removal and overall reconstruction quality. The key innovations of our work lie in its integration of hybrid uncertainty-based segmentation, dynamic rendering loss functions, and an optimized sliding window strategy, which collectively enhance robustness and efficiency in dynamic scene reconstruction. This approach offers a promising solution for real-time robotic applications, including autonomous navigation and augmented reality. Full article