Search Results (741)

This paper tackles the growing uncertainty in distribution networks caused by distributed generation, load fluctuations, and frequent topological changes. It proposes a multi-source data fusion framework using enhanced selective convolution (SKNet) and graph attention networks (GAT). First, heterogeneous measurement data, including Phasor Measurement Unit (PMU) and Supervisory Control and Data Acquisition (SCADA) data, are processed through a unified normalization and outlier elimination technique to ensure data quality. Second, SKNet is utilized to extract spatiotemporal multi-scale features, improving the detection of both rapid disturbances and long-term trends. Third, the extracted features are fed into GAT to model node electrical couplings, while power flow residual constraints are embedded in the loss function to enforce the physical validity of the estimated states. This physics-informed design overcomes a key limitation of pure data-driven models and enables an end-to-end framework that integrates data-driven learning with physical mechanism constraints. Finally, comprehensive validation is performed on the improved IEEE 33-node and IEEE 123-node test systems. The test scenarios include Gaussian measurement noise, data outliers, missing measurements, and topological changes. The results show that the proposed method outperforms baseline models such as Multi-Scale Graph Attention Network (MS-GAT), Bidirectional Long Short-Term Memory (BiLSTM), and traditional weighted least squares (WLS). It achieves Root Mean Square Error (RMSE) reductions of up to 18% and Mean Absolute Error (MAE) reductions of up to 15%. The average inference latency is only 10–18 ms. Even under unknown topological changes, the estimation error increases by only 15–25%. These results demonstrate the superior accuracy, robustness, and real-time performance of the proposed method for intelligent distribution network state estimation. Full article

Stable circumstances and an improved voltage profile need power compensators integrated with energy storage elements in AC power systems. The control of these compensators is of paramount importance for obtaining high accuracy, reliability, and better system dynamics, which involves careful controller design considerations and small-signal analysis. This paper focuses on the use of a static synchronous compensator (STATCOM) and supercapacitor energy storage system (SCESS) for achieving voltage stability, grid support, and better system dynamics. After the primary load is shifted to the grid, real power assistance is promptly injected into the AC grid to enhance the DC-link voltage, as well as the grid voltage, and reduce supply current from the grid using a vector control technique. The SCESS is handled with the help of a bidirectional DC–DC converter, which facilitates charging and discharging during boost and buck operations, respectively. Using small-signal modeling, the stable system is designed to obtain a reliable and stable output, which is confirmed by the systematic simulations and experiments. Full article

With the rapid advancement in information technology, the digital twin and smart assembly process simulation have become an integral part of the design and manufacturing of high-precision products. However, conventional Tunnel Boring Machine (TBM) cutterhead design and on-site assembly planning remain largely experience-driven and fragmented, with limited interoperability between geological characterization, structural verification, and constructability validation. This study proposes a digital twin-driven framework for TBM cutterhead design optimization and assembly process simulation that integrates geology-aware design inputs, BIM-based information modelling, FEM-based structural assessment, and immersive virtual environments within a unified virtual–physical workflow. To ensure consistent data exchange across platforms, an IFC4.3-compliant ontology is established using a non-intrusive property-set (Pset) extension strategy to represent cutterhead components, geological parameters, FEM load cases/results, and assembly tasks. Tunnel-scale stress analysis and cutter–rock interaction modelling are used to define project-representative cutter loading envelopes, which are mapped to a high-fidelity cutterhead FEM model for iterative structural refinement. The optimized configuration is then transferred to a game-engine/VR environment to support full-scale design inspection and assembly rehearsal, followed by manufacturing and field deployment with bidirectional feedback. To validate the proposed framework, an implementation case study of a deep hard-rock tunnelling project is presented where five design iterations were tracked across BIM–FEM–VR and nine constructability issues detected and resolved prior to assembly. The results indicate that the proposed digital twin approach strengthens traceability from geology to loading to structural response, reduces localized stress concentration at critical interfaces, and improves assembly readiness for complex tunnelling projects. Full article

Legged robots inspired by animal locomotion require actuators with high power density, fast response, and robust force control, yet traditional valve-controlled hydraulic systems suffer from substantial energy losses and weak regeneration performance. Motivated by role allocation across gait phases in animal legs, where in-air positioning requires far less actuation effort than ground contact support and force modulation, this work proposes a novel gas–oil hybrid servo actuator, denoted GOhsa, for quadruped knee joints. GOhsa utilizes pre-charged high-pressure gas to pressurize hydraulic oil, converting the conventional dual-chamber pressure servo control into a single-chamber configuration while preserving the original piston stroke. This architecture enables bidirectional position–force control, enhances energy regeneration applicability, and improves operational efficiency. Theoretical modeling is conducted to analyze hydraulic stiffness and frequency-response characteristics, and a linearization-based force controller with dynamic compensation is developed to handle system nonlinearities. Experimental validation on a single-leg platform demonstrates significant energy-saving performance: under no-load conditions (simulating the swing phase), GOhsa achieves a maximum power reduction of 79.1%, with average reductions of 15.2% and 11.5% at inflation pressures of 3 MPa and 4 MPa, respectively. Under loaded conditions (simulating the stance phase), the maximum reduction reaches 28.0%, with average savings of 10.0% and 9.8%. Tracking accuracy is comparable to traditional actuators, with reduced maximum errors (13.7 mm/16.5 mm at 3 MPa; 15.0 mm/17.8 mm at 4 MPa) relative to the 16.6 mm and 18.1 mm errors of the conventional system, confirming improved motion stability under load. These results verify that GOhsa provides high control performance with markedly enhanced energy efficiency. Full article

Vehicle-to-Grid (V2G) technology enables controlled bidirectional energy exchange between electric vehicles (EVs) and the power grid, allowing EVs to operate as flexible storage resources that support renewable-energy integration, peak-load reduction, and ancillary services. As EV adoption grows, deploying V2G at scale requires a comprehensive understanding of the electrochemical, power-electronic, communication, and mobility foundations that determine system performance. This review presents an integrated assessment of the essential components of V2G and broader Vehicle Grid Integration (VGI). First, the technical foundations are examined, including traction batteries, battery management systems, bidirectional converter topologies, charger architectures, connector standards, and grid-code compliance. Battery degradation mechanisms under V2G cycling are analyzed, with emphasis on depth of discharge, cycling frequency, and thermal conditions. Second, charging-infrastructure architectures and grid-integration considerations are evaluated across AC, DC, on-board, and off-board charging systems. Third, communication and interoperability frameworks, including ISO 15118, OCPP, OCPI, and cybersecurity requirements, are reviewed to assess the security and scalability of V2G operations. Finally, grid-aware mobility applications are discussed, covering coordinated charging, energy-aware routing, shared and autonomous mobility services, and dynamic pricing within coupled power and transport networks. The review concludes by identifying key technical and operational insights that support the development of robust V2G and VGI ecosystems. Full article

The rapid increase in electric vehicle (EV) adoption necessitates advanced charging infrastructures that are compact, efficient, and capable of bidirectional power flow for both vehicle-to-grid (V2G) and grid-to-vehicle (G2V) operation. Unlike traditional silicon and SiC-based chargers, this work introduces a Ga₂O₃-based bidirectional smart charging system integrated with a hybrid energy storage system to deliver superior performance. A coordinated control strategy is developed to regulate power sharing between a supercapacitor and a lithium-ion battery pack, thereby extending battery life, reducing current stress, and providing effective transient support. This hybrid system employs PI-based control and advanced modulation techniques to minimize current ripple, maintain the unity power factor, and ensure stable DC-link voltage regulation. MATLAB/Simulink simulation results demonstrate robust DC-link stability, smooth bidirectional power transfer, and very low total harmonic distortion. Comparative loss analysis shows that Ga₂O₃ MOSFETs offer significantly lower conduction and switching losses, enabling efficiencies up to 98% across the rated operating range. These results confirm that the proposed charger is highly suitable for next-generation EV infrastructures requiring high power density, reliable grid interfacing, and enhanced operational longevity. A hardware prototype was also developed and tested, with experimental results validating reliable grid-side performance and efficient energy sharing under typical operating conditions. Full article

High-speed electrical wireline links, spanning Serializer/Deserializer backplanes and cables, chip-to-chip and die-to-die interfaces, wide-parallel single-ended (SE) buses, and simultaneous-bidirectional (SBD) buses, increasingly operate under severe insertion loss, long channel memory, and strong multi-lane interference. Equalization is therefore a central enabler for reliable symbol recovery in the presence of inter-symbol interference (ISI), echo, and near-/far-end crosstalk. This review synthesizes recent principles, architectures, and silicon-proven implementations of wireline equalizers with an emphasis on practical hardware constraints. It further organizes key research trajectories in high-speed wireline communications across three domains: (i) Time-domain equalization and detection for ISI-limited channels, spanning feed-forward equalizers, latency-relaxed decision-feedback equalization architectures that mitigate stringent feedback-loop constraints, and partial-response signaling combined with reduced-complexity maximum-likelihood sequence detection to enhance resilience against extended channel memory. (ii) Advanced modulation and frequency-domain processing, marking the transition from conventional 4-level pulse-amplitude modulation toward higher-order constellations and multicarrier techniques, notably discrete multitone and orthogonal frequency-division multiplexing, which necessitates modulation-aware frequency-domain equalization and adaptive bit- and power-loading algorithms. (iii) Crosstalk and echo mitigation for dense SE and SBD systems, including cancellation filtering in a multiple-input multiple-output framework and coding-aided interference suppression approaches. Across these domains, we present the fundamental trade-offs between equalization performance, algorithmic convergence, power-area efficiency, and latency. Full article

Proton Exchange Membrane Fuel Cells (PEMFCs) are highly valued for their zero emissions, low noise, and environmentally friendly characteristics. However, they face substantial difficulties when starting up in low-temperature conditions. Coolant-assisted heating is usually more effective than other methods because of its fast speed, high heat transfer efficiency, and simple structure. This study developed a three-dimensional multiphase non-isothermal PEMFC cold start model with coolant-assisted heating. Key parameters, including heat consumption rate, coolant flow rate, load current slope, initial membrane water content, catalyst layer porosity, and gas diffusion layer porosity, were selected as optimization variables. A Convolutional Neural Network–Attention Mechanism–Bidirectional Long Short-Term Memory Neural Network (CAB-Net) was employed as a surrogate model to predict the ice volume fraction during the cold start process. The CAB-Net model was further integrated with the Lexicographic Ordered Whale Optimization Algorithm (LO-WOA) to identify the optimal combination of parameters. The optimization aimed to minimize the maximum ice volume fraction (MIVF) in the Cathode Catalyst Layer (CCL) and reduce the energy consumption required to reach this fraction. The optimization results revealed that, compared to the baseline model (MIVF = 0.4519, energy consumption = 0.77264 J), the MIVF was reduced to 0.1471, representing a 67.45% decrease, while energy consumption was reduced to 0.70299 J, achieving a 9.01% decrease. The results underscore the efficacy of the proposed strategy in enhancing cold start performance under low-temperature conditions. Full article

Nonlinear loads are widely applied, making the generation mechanism of grid harmonics increasingly intricate. However, high-precision monitoring devices suffer from high deployment costs and limited coverage. This poses a major challenge to directly acquiring harmonic voltages at some nodes. To solve this problem, this paper proposes a harmonic state estimation method based on a Dual-Stream Adaptive Fusion Generative Adversarial Network (DSAF-GAN), with an innovative design in its generator architecture. A dual-path generator is developed to extract multi-scale features through heterogeneous network branches collaboratively. The ResNet-GRU path integrates convolutional residual modules with Bidirectional Gated Recurrent Units (Bi-GRUs). It effectively captures local spatial patterns and temporal dynamic characteristics of time-series data. The multi-layer perceptron (MLP) path focuses on mining global nonlinear correlations, thereby enhancing the overall feature-expressing capability. An adaptive weight fusion module (Attention Weight Net) fuses the outputs of the two paths. It dynamically allocates contribution weights, improving the model’s flexibility and generalization performance. Experimental results show that the proposed DSAF-GAN can accurately reconstruct the harmonic voltage component content rate of missing nodes. Full article

Chikungunya virus (CHIKV) is now prevalent in multiple regions worldwide, posing a serious threat to human health. In this study, we have successfully established a rhesus macaque model of Chikungunya virus infection. Notably, while no viral load was detected in liver tissue on day 7 post-infection, significant pathological damage was observed. Single-cell RNA sequencing of liver tissue revealed a reduction in B cells following infection. Among T cells, CD8⁺ T and NKT cells mediated major cytotoxic effects, whereas CD4⁺ T and memory T cells primarily exerted regulatory functions that further enhanced the activation of CD8⁺ T and NKT cells. In macrophages, inflammatory macrophages fc gamma R-mediated phagocytosis upregulated, with multiple key activation-related genes being highly upregulated. Furthermore, we observed that there might be a potential bidirectional activation effect between T cells and macrophages. These results indicate that CHIKV-induced indirect liver injury is likely mediated not only by the virus itself but also, in part, by the activation of hepatic immune cells. Full article

Short-term electricity load forecasting has become increasingly challenging due to growing demand volatility, nonlinear load patterns, and the dynamic penetration of renewable energy sources. Conventional forecasting models often suffer from sensitivity to hyperparameter settings and limited capability in capturing long-term temporal dependencies. To address these issues, this paper proposes a hybrid forecasting framework that integrates an Improved Snow Ablation Optimizer (ISAO) with a Dilated Bidirectional Gated Recurrent Unit (Dilated BiGRU). The proposed ISAO enhances the original Snow Ablation Optimizer through three key strategies to improve performance in high-dimensional optimization problems: (i) a subgroup cooperative mechanism to alleviate cross-dimensional interference, (ii) a learning-automata-based adaptive dimension assignment strategy to dynamically allocate optimization resources, and (iii) a t-distribution-based adaptive step size mechanism to balance global exploration and local exploitation. Extensive experiments on the CEC2017 benchmark suite demonstrate that ISAO achieves superior convergence speed and optimization accuracy, with average rankings of 1.60, 1.77, and 2.03 on 30-, 50-, and 100-dimensional problems, respectively, significantly outperforming the original SAO and several state-of-the-art metaheuristic algorithms. Building upon this optimization capability, ISAO is employed to automatically tune the key hyperparameters of the Dilated BiGRU model. Experiments conducted on the Kaggle electricity load dataset show that the proposed ISAO-Dilated BiGRU model achieves MAE, MAPE, and RMSE values of 20.003, 1.711%, and 25.926, respectively, corresponding to reductions of 16.6%, 15.6%, and 17.7% compared with the baseline model, along with an R² of 0.97841. Comparative results against RNN, LSTM, Random Forest, and the original Dilated BiGRU confirm the robustness and superior long-term dependency modeling capability of the proposed framework. Overall, the proposed ISAO effectively enhances hyperparameter optimization quality and significantly improves the predictive accuracy and stability of the Dilated BiGRU model, providing a reliable and practical solution for short-term electricity load forecasting in modern power systems. Full article

This study explores the feasibility of estimating tibiofemoral joint contact forces using deep learning models trained on in vivo biomechanical data. Leveraging the comprehensive CAMS-Knee datasets, we developed and evaluated two machine learning network architectures, a bidirectional Long Short-Term-Memory Network with a Multilayer Perceptron (biLSTM-MLP) and a Temporal Convolutional Network (TCN) model, to predict medial and lateral knee contact forces (KCFs) across various activities of daily living. Using a leave-one-subject-out validation approach, the biLSTM-MLP model achieved root mean square errors (RMSEs) as low as 0.16 body weight (BW) and Pearson correlation coefficients up to 0.98 for the total KCF (${\mathrm{F}}_{\mathrm{tot}}$) during walking. Although the prediction of individual force components showed slightly lower accuracy, the model consistently demonstrated high predictive accuracy and strong temporal coherence. In contrast to the biLSTM-MLP model, the TCN model showed more variable performance across force components and activities. Leave-one-feature-out analyses underscored the dominant role of lower-limb kinematics and ground reaction forces in driving model accuracy, while EMG features contributed only marginally to the overall predictive performance. Collectively, these findings highlight deep learning as a scalable and reliable alternative to traditional musculoskeletal simulations for personalized knee load estimation, establishing a foundation for future research on larger and more heterogeneous populations. Full article

As a key structural component of rockfill dams, the load-bearing capacity of large-sized concrete slabs under complex multi-axial stresses is directly related to the long-term safe operation of the dams. This study conducted uniaxial and biaxial lateral compression strength tests on C25 concrete slabs with dimensions of 1500 × 1500 × 150 mm using a large-scale bi-directional loading reaction frame test system, systematically revealing the mechanical properties and failure criteria of large-sized concrete slabs. The results indicate that the biaxial compressive strength of the concrete slabs is significantly greater than the uniaxial compressive strength. The stress–strain curves of the concrete slabs and standard specimens exhibit good consistency before failure. Based on uniaxial compressive strength data, the concrete size effect strength reduction formula proposed by Neville was modified, and a compressive strength prediction formula applicable to large-sized concrete members was established. Further integration with code-specified failure criteria led to the development of a biaxial failure envelope for large-sized concrete slabs, which was validated to agree well with measured data. The research findings can provide reliable experimental evidence and theoretical support for the strength reduction, load-bearing capacity assessment, and revisions of relevant design codes for large hydraulic components such as concrete face slabs in rockfill dams. Full article

Reciprocating compressors, which serve as core equipment in the petrochemical and natural gas transmission sectors, operate under prolonged variable loads and high-frequency impact conditions. Critical components, such as valves and piston rings, are prone to failure. Existing fault diagnosis methods suffer from inadequate spatio-temporal feature extraction and neglect spatio-temporal correlations. To address this, this paper proposes a spatio-temporal feature fusion-based fault diagnosis method for reciprocating compressors. This method constructs a spatio-temporal feature fusion model (STFFM) comprising three principal modules: First, a spatio-temporal feature extraction module employing a multi-layered stacked bidirectional gated recurrent unit (BiGRU) with batch normalisation to uncover temporal dependencies in long-term sequence data. A graph structure is constructed via k-nearest neighbours (KNN), and an enhanced graph isomorphism network (GIN) is integrated to capture spatial domain fault information variations. Second, the spatio-temporal bidirectional attention-gated fusion module employs a bidirectional multi-head attention mechanism to enhance temporal and spatial features. It incorporates a cross-modal gated update mechanism and learnable weight parameters to dynamically retain the highly discriminative features. Third, the classification output module enhances the model’s generalisation capability through multi-layer fully connected layers and regularisation design. Research findings demonstrate that this approach effectively integrates spatio-temporal coupled fault features, achieving an average accuracy of 99.14% on an experimental dataset. This provides an effective technical pathway for the precise identification of faults in the critical components of reciprocating compressors. Full article

To improve DC-bus voltage regulation of bidirectional DC/DC converters in photovoltaic–energy storage DC microgrids, this paper proposes an improved linear active disturbance rejection control (LADRC) strategy based on observation error reconstruction. In conventional LADRC, the linear extended state observer (LESO) is driven solely by the output tracking error, which may lead to weakened disturbance excitation after rapid error convergence and thus degraded disturbance estimation performance. To address this limitation, an observation error reconstruction mechanism is introduced, in which a reconstructed error variable incorporating higher-order estimation deviation information is used to redesign the LESO update law. This modification fundamentally enhances the disturbance-driving mechanism without excessively increasing observer bandwidth, resulting in improved mid- and high-frequency disturbance estimation capability. The proposed method is analyzed in terms of disturbance estimation characteristics, frequency-domain behavior, and closed-loop stability. Comparative simulations and hardware-in-the-loop experiments under typical load and photovoltaic power step variations within the safe operating range demonstrate that the proposed LADRC–PI significantly outperforms conventional PI and LADRC–PI control. Experimental results show that the maximum DC-bus voltage fluctuation is reduced by over 60%, and the voltage recovery time is shortened by approximately 40–50% under the tested operating conditions. Full article