Search Results (30,523)

In robotic grinding of complex curved surfaces, the low stiffness of serial robots causes tool tip deflection and degrades surface quality. The axial symmetry of grinding discs introduces a free rotational parameter at each waypoint, converting a standard 6-DOF robot into a functionally redundant system. However, this redundancy has not been systematically exploited for stiffness optimization along the trajectory. This paper proposes a hierarchical dynamic programming framework to optimize the redundancy angle sequence over the entire grinding trajectory. A kinematic transformation parameterizes the flange target by the redundancy angle, enabling enumeration of feasible candidate configurations over a discretized grid. A composite stiffness index that accounts for the normal, feed, and cross-feed grinding force components is formulated at the contact point. Hierarchical constraint filtering removes configurations that violate posture, singularity, velocity, acceleration, and stiffness constraints. The Viterbi algorithm then recovers the minimum-cost path that balances stiffness performance and joint motion smoothness. Finally, a post-processing step based on a cubic smoothing spline generates $C^2$-continuous joint trajectories. Simulations on a UR5 robot grinding a curved surface evaluate the proposed framework against fixed-angle, greedy, and flange-stiffness baselines. The proposed method improves the mean composite stiffness by 31.7% and 17.9% over the fixed-angle and flange-stiffness baselines, respectively, and reduces the maximum joint jump by two orders of magnitude compared with the greedy strategy. Experimental validation on a UR5 robot confirms that the smoothed trajectory is accurately tracked while the stiffness threshold is preserved. A multi-trajectory analysis further shows that the stiffness threshold is maintained across all grinding trajectories. These results demonstrate the effectiveness of the proposed framework for redundancy optimization in robotic grinding with tool spin symmetry. Full article

This study addresses the need for intelligent condition monitoring in high-complexity medical imaging systems by proposing a smart sensing architecture for the Revolution EVO Computed Tomography (CT) scanner. Ensuring operational reliability and minimizing unexpected downtime remain critical challenges in advanced CT platforms, motivating the integration of distributed sensing and data-driven analytics. System logs spanning August 2024 to October 2025 were processed into 10-min intervals and converted into a structured dataset comprising 76 features derived from operational events, scanning parameters, and temporal dynamics. Two supervised learning models, the Support Vector Machine (SVM) and Artificial Neural Network (ANN), were trained to identify abnormal operating conditions. Both models delivered excellent classification performance, achieving an accuracy of 0.973. The SVM demonstrated balanced precision, recall, and F1-score metrics of 0.973, while the ANN outperformed in ranking and sensitivity to anomalies with an AUROC of 0.993 and an AUPRC of 0.976. This framework highlights the potential of sensor-driven machine learning in enabling early detection of system anomalies and optimizing maintenance planning within clinical CT environments. Full article

The multi-cycle high-rate injection and production operations in Underground Gas Storage (UGS) facilities converted from depleted fracture-pore carbonate gas reservoirs induce complex rock–fluid interactions that threaten long-term integrity and performance. This study experimentally investigates the petrophysical responses of the Xiangguosi (XGS) UGS carbonate reservoirs in China using multi-cycle stress sensitivity tests, fines migration experiments, and water evaporation–salt precipitation analyses. SEM observations distinguish the contributions of crack closure and matrix compression to permeability evolution. Results show a sharp contrast in mechanical damage: high-quality rocks present negligible permanent deformation (<8% Young’s modulus reduction), whereas poor-quality rocks suffer catastrophic deterioration (>60%). Fines migration exhibits a three-stage behavior under cyclic flow, with water saturation significantly aggravating permeability impairment. A critical salinity threshold (220,000 ppm) is identified for the transition between drying-enhanced storage and salt plugging. Permeability declines sharply despite a slight porosity increase due to selective salt clogging of key pore throats, revealing a clear porosity–permeability decoupling. Salt deposition under movable water conditions can reduce UGS capacity by up to 1.45%. Reservoir heterogeneity, microfractures, karst structures, and initial petrophysical properties dominate the storage and flow space evolution. This work provides a predictive framework for optimizing injection–production strategies and improving the performance of complex carbonate UGS. Full article

Commutation failures (CFs), which occur when current transfer between valves in line-commutated converter high-voltage direct current (LCC-HVDC) systems is disrupted, pose a challenge in weak alternating current (AC) networks. This paper introduces a coordinated control strategy that combines a fuzzy self-tuning proportional-integral (PI) controller (FSTPIC) and a static synchronous compensator (STATCOM) device to mitigate CFs and enhance system stability. The approach applies the FSTPIC to both converters of the HVDC link, while the STATCOM at the inverter side delivers dynamic reactive power and voltage support during AC faults. We test this strategy on the CIGRE HVDC benchmark system using MATLAB/SIMULINK simulations. The results demonstrate that the proposed method significantly reduces CFs, mitigates transient oscillations, and shortens recovery time compared to conventional control techniques. This coordinated control boosts voltage stability and the system’s ability to ride through faults, confirming its superiority under various fault scenarios in weak-grid conditions. Full article

The CO₂ storage–regeneration (CO₂-SR) process represents a promising strategy for integrating CO₂ capture and catalytic conversion within a single cyclic operation using multifunctional catalysts. In this concept, CO₂ is first stored on basic sites and subsequently converted through methane activation, enabling the coupling of CO₂ capture and reforming reactions in a single reactor. In this work, a series of unsupported Ni–Ba catalysts were investigated as model multifunctional materials for the CO₂-SR process. Catalysts with different Ni/Ba ratios were prepared to analyze how the distribution of storage and catalytic sites influences the cyclic CO₂ capture–conversion behavior. In addition, Rh was introduced as a promoter either during synthesis by co-precipitation or ex situ by impregnation, allowing to evaluate the influence of Rh location and surface enrichment on the catalytic properties. Rh incorporation in the NiBa catalyst (Ni/Ba = 10/1 and Ni/Rh = 100/1) increased the specific surface area (BET area 64 m²·g⁻¹ vs. 55 m²·g⁻¹ for NiBa) and reduced the NiO crystallite size from 250.4 Å to 231.5 Å, indicating improved dispersion of the metallic phase. XPS analysis revealed the coexistence of Rh⁰ and Rh³⁺ species, suggesting that Rh acts as a redox mediator that facilitates hydrogen activation and promotes hydrogen spillover to neighboring Ni sites. Raman and CO₂-TPD results show that Ba-derived domains stabilize carbonate species responsible for CO₂ storage, while Rh enhances catalyst reducibility and modifies the kinetics of carbonate decomposition during the regeneration stage. Transient CO₂–CH₄ pulse experiments demonstrate that the CO₂-SR process proceeds through a dynamic surface cycle involving reversible carbonate formation on Ba-derived basic sites coupled with methane activation on Ni-containing interfacial sites. The results indicate that catalyst performance is governed by a hierarchical surface architecture composed of Ni–O–Ba interfacial domains, reversible Ba–O–Ba carbonate storage sites, and more stable Ba-rich domains. The distribution of these domains, controlled by the Ni/Ba ratio and the dispersion of the metallic phase, determines the reversibility of carbonate formation and the efficiency of the cyclic CO₂ storage–regeneration process. Full article

Bidirectional power converters generate significant heat losses during high-frequency operation, posing a severe challenge to the performance of heat dissipation systems. Traditional thermal design methods often struggle to balance multiple objectives, such as cooling efficiency, cost, weight, and size, thereby limiting the reliability and safety of the system. To address these challenges, this paper proposes a novel Multi-Objective Exponential Distribution Optimizer algorithm based on the Exponential Distribution Optimizer. Subsequently, key design variables of the heat dissipation system are selected. Next, the Optimal Latin Hypercube Sampling method is employed to generate sample points, and a second-order response surface surrogate model for the heat pipe radiator’s volume and temperature is developed. Lastly, by integrating elite non-dominated sorting, crowding distance mechanisms, and an information feedback mechanism, the multi-objective challenge is decomposed into subproblems, thereby enhancing optimization efficiency. Through comparative simulation experiments on benchmark functions, the Wilcoxon signed-rank test results for the MOEDO algorithm on the majority of the three metrics are denoted as ‘+’, indicating statistically significant advantages over the compared algorithms, thereby demonstrating its superior performance in addressing multi-objective optimization problems. The study further conducts simulation verification of the heat pipe heat dissipation system before and after optimization using ANSYS Icepak. The simulation results demonstrate that, compared with the conventional design, the maximum Insulated Gate Bipolar Transistor (IGBT) temperature is reduced by 17.12% and the heat sink volume is reduced by 14.61%. Full article

Time-series clustering is a prominent research area with extensive practical applications. Given the complexity and diversity of modern time-series data, this study proposes a novel time-series clustering method based on inter-network heterogeneity. First, each time-series is converted into a network by using two types of time-series segmentation techniques. Second, an inter-network clustering approach based on graph spectral theory is introduced: we calculate the total variation (TV) distance between the empirical spectral distributions of each network and identify distinct clusters using a hierarchical clustering algorithm. From the perspective of symmetry, networks constructed from similar time-series tend to exhibit comparable spectral structures, which reflect the underlying structural symmetries of their dynamics. Differences in spectral distributions correspond to symmetry breaking among networks, providing an effective mechanism for distinguishing heterogeneous time-series patterns. Our method effectively preserves more distinctive features inherent in the original time-series. To evaluate the performance of the proposed method, simulation studies are conducted, including the recognition of both stationary and non-stationary sequences. The method also performs well on real-world datasets, such as stock closing prices. These results demonstrate that our approach can handle non-stationary sequences and identify the intrinsic correlations in time-series. Full article

Traditional wood identification models are vulnerable to out-of-distribution (OOD) substitution in the global timber trade. In response to this issue, this study presents a dual-stage cascade authentication architecture using in situ Raman spectroscopy and machine learning. First, a physically informed preprocessing strategy, integrating adaptive truncation (>1749 cm⁻¹) and first-derivative filtering, is developed to extract a 1309-dimensional composite feature matrix. This step effectively decouples non-linear fluorescence and converts physical detector saturation into highly discriminative features. To mitigate data leakage, the system utilizes a cross-validated Random Forest engine for Stage-1 closed-set discriminative screening. Subsequently, it cascades a high-dimensional One-Class Support Vector Machine (OCSVM) for Stage-2 open-set non-linear boundary verification in the Reproducing Kernel Hilbert Space. This design avoids the “variance trap” of traditional linear dimensionality reduction (e.g., PCA), preserving weak but critical secondary metabolite signals. Under a controlled OOD benchmarking scenario involving three taxonomically and chemically similar substitute species, the optimized Stage-1 engine maintains a 91.67% closed-set accuracy on known species. Crucially, Stage-2 verification achieves an open-set detection AUROC of 0.9722 and limits the FPR95 to 3.33%. Feature importance mapping indicates that the model effectively incorporates macroscopicoptical surrogate features (e.g., fluorescence decay boundaries) for decision-making. Overall, this study offers a robust, controlled non-destructive approach for real-world wood authenticity verification. Full article

Synchronous Alternating Current (AC) power systems are increasingly supported by embedded High-Voltage Direct Current (HVDC) links to enhance operational flexibility and ensure security of supply. However, the loss of High-Voltage Alternating Current (HVAC) interconnections in these synchronous areas may lead to transmission network splitting, posing serious challenges to frequency stability due to the reduction in overall system inertia and stiffness. In this paper, a supplementary control layer is proposed to enable embedded HVDC systems, particularly those based on modern Voltage Source Converters (VSCs), to support frequency stability under post-splitting conditions. The proposed control strategy combines Angle-Difference Control (ADC), Frequency-Difference Control (FDC), and feedforward action, enabling fast and coordinated active-power modulation. A single-bus, dynamic multi-area Load Frequency Control (LFC) model is developed, combining the regulation of thermal units, Renewable Energy Sources’ (RESs’) Fast Frequency Response (FFR) with Synthetic Inertia (SI), and VSC-HVDC modulation. The effectiveness of the proposed control layer is demonstrated by applying it to the East Tyrrhenian Link (ETL), an embedded VSC-HVDC interconnection connecting Sicily with the mainland of Italy, under a post-splitting low-inertia condition in which Sicily operates as an islanded synchronous system, i.e., after losing synchronism with the mainland of Italy, in a 2030 scenario condition. The simulation results demonstrate that the proposed controller enables embedded VSC-HVDC systems to actively participate in post-splitting frequency containment and damping, as well as coordinated active power reallocation, thereby enhancing overall system stability and resilience. Full article

Trans-anethole oxygenase (TAO) exhibits broad arylpropene substrate specificity but has low activity in converting isoeugenol to high-value vanillin. Herein, we employed computer-aided rational design to engineer TAO from Pseudomonas putida (PpTAO) for enhanced catalytic efficiency toward isoeugenol. Structural modeling and AlphaFold 3 docking identified two key catalytic residues, Arg86 and His118. Through substrate channel engineering and computation-guided mutagenesis, a series of targeted variants were constructed. Three variants, H93A, Q207R/G249C, and I59T/F62T, showed significant improvements in whole-cell performance, with activity increases of 1.8-, 2.13-, and 4.83-fold over the wild type (WT), respectively. Purified enzyme kinetics corroborated these findings, as reflected in k_cat/K_m values that reached 1.6, 2.1, and 4.7 times that of the WT. Mechanistic molecular dynamics simulations revealed that H93A enhances activity by widening the access tunnel, whereas Q207R/G249C exerts beneficial distal effects. Notably, the I59T/F62T variant significantly increases substrate affinity by optimizing hydrophobic interactions within the binding pocket. These results validate the efficacy of computational modeling in enzyme redesign and provide a robust biocatalyst for the sustainable biosynthesis of vanillin. Full article

Infrared small-target detection (IRSTD) has benefited from deep learning, yet most existing methods still rely on dense pixel-level annotations, which are costly and often unreliable for tiny and weak targets. Point supervision offers a more practical alternative, but current point-supervised methods usually construct pseudo-labels based on the distance between pixels and annotated points or cluster centers, which introduces spatial bias and may miss genuine target pixels away from these reference points. To address this issue, we propose GMVG-DIR, a point-supervised IRSTD framework that combines Gradient-Guided Minimum Variance Growth (GMVG) with Deep Iterative Refinement (DIR). GMVG first estimates target likelihood from gradient-guided aggregation of contour closure and edge responses and then converts it into structurally coherent pseudo-labels via the Minimum Variance Growth filter, without relying on distance cues. DIR further improves the pseudo-labels by incorporating reliable semantic guidance into an iterative refinement process, thereby reducing error propagation. By emphasizing structural consistency rather than spatial proximity, the proposed framework better preserves irregular target shapes and remains robust to point-label deviation. Extensive experiments on NUDT-SIRST, IRSTD-1k, and NUAA-SIRST show that GMVG-DIR improves pseudo-label fidelity and achieves competitive point-supervised performance across multiple dataset-backbone settings, especially in IoU and $P_d$. Full article

We propose a monolithically integrated optical frequency comb (OFC) generation platform on thin-film lithium niobate (TFLN), featuring cascaded dual-drive Mach–Zehnder modulators (DDMZM) and a ${\mathrm{S}\mathrm{i}}_3{\mathrm{N}}_4$-assisted spot size converter (SSC). To capture microscopic mode mismatches and spatial phase accumulation often overlooked in idealized scalar simulations, we establish a multi-physics co-simulation framework integrating finite-difference time-domain (FDTD) analysis with macroscopic transmission modeling. Based on this framework, the cascaded modulator architecture generates 25 highly stable comb lines with a dense 2 GHz spacing and an envelope flatness within 2 dB. Tolerance analysis indicates that the comb generation is highly resilient to typical manufacturing and environmental variations, including thermal bias drift, RF phase mismatch, and half-wave voltage ($V_{\pi }$) dispersion. Furthermore, physical-layer modeling shows that the integrated SSC reduces fiber-to-chip coupling loss to 0.55 dB per facet, preserving the necessary optical power budget. To validate the platform’s viability as a multi-wavelength continuous-wave source for spatial-division multiplexed (SDM) interconnects, a parallel transmission over a 20 km standard single-mode fiber is modeled. Using a digital signal processing (DSP)-free 10 Gb/s non-return-to-zero (NRZ) scheme, the 25-channel system maintains a worst-case bit error rate strictly below the forward error correction (FEC) threshold. This work offers a practical, physics-based evaluation framework for high-density co-packaged optics (CPO). Full article

A strategy for controlling DC boost converters under disturbances is proposed through a disturbance-resistant reinforcement learning method. An iterative training process is designed using model-free deep reinforcement learning. A nonlinear disturbance observer is integrated to enhance the controller’s adaptability to disturbances. The proposed neural network controller achieves a maximum voltage deviation of 4.2% of the reference value, and a settling time of 6.5 ms in simulation studies. During experimental validation, the voltage deviation is limited to 6.0% with a settling time of 8 ms under the same operating condition. Simulation and experimental results demonstrate that the proposed control strategy is able to stabilize the output voltage under various disturbances, offering better performance under large-signal fluctuation. This work extends the practical deployment of reinforcement learning in the field of nonlinear control. Full article

Thermal monocular depth estimation can provide more robust depth predictions than RGB-based methods under nighttime and adverse weather conditions. However, when trained with projected LiDAR supervision, depth models often retain structural errors in sky regions, long-range areas, and object boundaries because LiDAR measurements are sparse or missing in such regions. To address this limitation, we propose a thermal monocular depth estimation framework that incorporates propagation-based refinement. To make this refinement applicable across different base models, we further design a multi-scale feature adapter that converts heterogeneous multi-scale features with different spatial resolutions and channel dimensions into a unified representation. As a result, the same refinement architecture can be used across different base models without model-specific refiner redesign. On the multispectral stereo (MS2) dataset, the proposed method improves both BTS (big-to-small) and NeWCRFs (neural window fully connected CRFs), reducing the meter-based error metrics SqRel from 0.380 to 0.369 and RMSE from 3.163 to 3.126 for BTS, and reducing SqRel from 0.331 to 0.328 and RMSE from 2.937 to 2.924 for NeWCRFs. Qualitative results further show that the proposed method alleviates mixed-depth artifacts and abnormal depth patterns in regions lacking reliable depth supervision. Full article

RUL prediction methods do not fully account for the uncertainties caused by data scarcity and inherent noise, and they also suffer from low reliability of RUL point estimates. To tackle these challenges, this paper proposes a Bayesian convolutional neural network with dual-output units for RUL interval predictions. The network employs the negative log-likelihood as the loss function. Thanks to its dual-output structure, it not only provides point estimates, but also quantifies the aleatoric uncertainty inherent in the data. During the training process, the CNN is reformulated using Bayesian principles, and the Bayes-by-backprop method is applied to train the network. This transformation converts model parameters from fixed values into random variables. As a result, epistemic uncertainty caused by model inaccuracies and limited data can be quantified. Experimental validation on the IEEE PHM Challenge 2012 dataset demonstrated that the proposed method achieved a higher prediction accuracy than state-of-the-art uncertainty-aware prediction approaches, demonstrating a better applicability in engineering practice. Full article