Search Results (371)

To enhance the reliability of electric propulsion in electric aircraft and address power interruptions caused by current sensor failures, this study proposes a current sensorless fault-tolerant control strategy for permanent magnet synchronous motors (PMSMs) based on a long short-term memory (LSTM) network. First, a hierarchical architecture is constructed to fuse multi-phase electrical signals in the fault diagnosis layer (sliding mode observer). A symbolic function for the reaching law observer is designed based on Lyapunov theory, in order to generate current predictions for fault diagnosis. Second, when a fault occurs, the system switches to the LSTM reconstruction layer. Finally, gating units are used to model nonlinear dynamics to achieve direct mapping of speed/position to phase current. Verification using a physical prototype shows that the proposed method can complete mode switching within 10 ms after a sensor failure, which is 80% faster than EKF, and its speed error is less than 2.5%, fully meeting the high speed error requirements of electric aircraft propulsion systems (i.e., ≤3%). The current reconstruction RMSE is reduced by more than 50% compared with that of the EKF, which ensures continuous and reliable control while maintaining the stable operation of the motor and realizing rapid switching. The intelligent algorithm and sliding mode control fusion strategy meet the requirements of high real-time performance and provide a highly reliable fault-tolerant scheme for electric aircraft propulsion. Full article

This paper presents a data-driven energy management scheme for fuel cell and battery electric vehicles, formulated as a constrained optimal control problem. The proposed method employs a co-state network trained using real-time measurements to estimate the control law without requiring prior knowledge of the system model or a complete dataset across the full operating domain. In contrast to conventional reinforcement learning approaches, this method avoids the issue of high dimensionality and does not depend on extensive offline training. Robustness is demonstrated by treating uncertain and time-varying elements, including power consumption from air conditioning systems, variations in road slope, and passenger-related demands, as unknown disturbances. The desired state of charge is defined as a reference trajectory, and the control input is computed while ensuring compliance with all operational constraints. Validation results based on a combined driving profile confirm the effectiveness of the proposed controller in maintaining the battery charge, reducing fluctuations in fuel cell power output, and ensuring reliable performance under practical conditions. Comparative evaluations are conducted against two benchmark controllers: one designed to maintain a constant state of charge and another based on a soft actor–critic learning algorithm. Full article

The rapid electrification of transportation and the proliferation of rooftop solar photovoltaics (PVs) in urban environments are reshaping the operational dynamics of power distribution networks. However, the inherent uncertainty in electric vehicle (EV) behavior—including arrival times, charging preferences, and state-of-charge—as well as spatially and temporally variable solar generation, presents a profound challenge to existing scheduling frameworks. This paper proposes a novel data-driven distributionally robust optimization (DDRO) framework for solar-powered EV charging coordination under spatiotemporal uncertainty. Leveraging empirical datasets of EV usage and solar irradiance from a smart city deployment, the framework constructs Wasserstein ambiguity sets around historical distributions, enabling worst-case-aware decision-making without requiring the assumption of probability laws. The problem is formulated as a two-stage optimization model. The first stage determines day-ahead charging schedules, solar utilization levels, and grid allocations across an urban-scale distribution feeder. The second stage models real-time recourse actions—such as dynamic curtailment or demand reshaping—after uncertainties are realized. Physical grid constraints are modeled using convexified LinDistFlow equations, while EV behavior is segmented into user classes with individualized uncertainty structures. The model is evaluated on a modified IEEE 123-bus feeder with 52 EV-PV nodes, using 15 min resolution over a 24 h horizon and 12 months of real-world data. Comparative results demonstrate that the proposed DDRO method reduces total operational costs by up to 15%, eliminates voltage violations entirely, and improves EV service satisfaction by more than 30% relative to deterministic and stochastic baselines. This work makes three primary contributions: it introduces a robust, tractable optimization architecture that captures spatiotemporal uncertainty using empirical Wasserstein sets; it integrates behavioral and physical modeling within a unified dispatch framework for urban energy-mobility systems; and it demonstrates the value of robust coordination in simultaneously improving grid resilience, renewable utilization, and EV user satisfaction. The results offer practical insights for city-scale planners seeking to enable the reliable and efficient electrification of mobility infrastructure under uncertainty. Full article

Renewable energy sources (RES) are the foundation of the ongoing energy transition in Poland and worldwide. However, increased use of RES has brought several challenges, as most of these sources are dependent on weather conditions. The instability and lack of control over electricity production lead to both overloads and power shortages in transmission and distribution networks. A significant advantage of biogas plants over sources such as photovoltaics or wind turbines is their ability to control electricity generation and align it with actual demand. Biogas produced during fermentation can be temporarily stored in a biogas tank above the digester and later used in an enlarged CHP unit to generate electricity and heat during peak demand periods. While demand-driven biogas plants operate similarly to traditional installations, their development requires navigating regulatory and administrative procedures, particularly those related to the grid connection of the generated electricity. In Poland, it has only recently become possible to obtain grid connection conditions for such installations, following the adoption of the Act of 28 July 2023, which amended the Energy Law and certain other acts. However, the biogas sector still faces challenges, particularly the need for effective incentive mechanisms and the removal of regulatory and economic barriers, especially given its estimated potential of up to 7.4 GW. Full article

As China’s economy grows and travel demand increases, its aviation market has evolved to become the second-largest in the world. This study presents a pioneering analysis of China’s aviation network evolution (1990–2024) by integrating temporal dynamics into a network density matrix theory, addressing critical gaps in prior static network analyses. Unlike conventional studies focusing on isolated topological metrics, we introduce a triangulated methodology: ① a network sequence analysis capturing structural shifts in degree distribution, clustering coefficient, and path length; ② novel redundancy–entropy coupling quantifying complexity evolution beyond traditional efficiency metrics; and ③ economic-network coordination modeling with spatial autocorrelation validation. Key innovations reveal previously unrecognized dynamics: ① Time-embedded density matrices ($\rho$) demonstrate how sparsity balances information propagation efficiency ($\eta$) and response diversity, resolving the paradox of functional yet sparse connectivity. ② Redundancy–entropy synergy exposes adaptive trade-offs. Entropy ($H$) rises 18% (2000–2024), while redundancy ($R$) rebounds post-2010 (0.25→0.33), reflecting the strategic resilience enhancement after early efficiency-focused phases. ③ Economic-network coupling exhibits strong spatial autocorrelation ($Moran’sI>0.16$, $p<0.05$), with eastern China achieving “primary coordination”, while western regions lag due to geographical constraints. The empirical results confirm structural self-organization. Power-law strengthening, route growth exponentially outpacing cities, and clustering ($C$) rising 16% as the path length ($L$) increases, validating the hierarchical hub formation. These findings establish aviation networks as dynamically optimized systems where economic policies and topological laws interactively drive evolution, offering a paradigm shift from descriptive to predictive network management. Full article

A scheme of load frequency control (LFC) is proposed based on the deep deterministic policy gradient (DDPG) and active disturbance rejection control (ADRC) for multi-region interconnected power systems considering the renewable energy sources (RESs) and energy storage (ES). The dynamic models of multi-region interconnected power systems are analyzed, which provides a basis for the subsequent RES access. Superconducting magnetic energy storage (SMES) and capacitor energy storage (CES) are adopted due to their rapid response capabilities and fast charge–discharge characteristics. To stabilize the frequency fluctuation, a first-order ADRC is designed, utilizing the anti-perturbation estimation capability of the first-order ADRC to achieve effective control. In addition, the system states are estimated using a linear expansion state observer. Based on the output of the observer, the appropriate feedback control law is selected. The DDPG-ADRC parameter optimization model is constructed to adaptively adjust the control parameters of ADRC based on the target frequency deviation and power deviation. The actor and critic networks are continuously updated according to the actual system response to ensure stable system operation. Finally, the experiment demonstrated that the proposed method outperforms traditional methods across all performance indicators, particularly excelling in reducing adjustment time (45.8% decrease) and overshoot (60% reduction). Full article

State estimation in distribution power systems is increasingly challenged by the proliferation of distributed energy resources (DERs), bidirectional power flows, and the growing complexity of unbalanced network topologies. Physics-Informed Neural Networks (PINNs) offer a compelling solution by integrating machine learning with the physical laws that govern power system behavior. This paper introduces a PINN-based framework for state estimation in unbalanced distribution systems, leveraging available data and embedded physical knowledge to improve accuracy, computational efficiency, and robustness across diverse operating scenarios. The proposed method is evaluated on four IEEE test feeders—IEEE 13, 34, 37, and 123—using synthetic datasets generated via OpenDSS to emulate realistic operating scenarios, and demonstrates significant improvements over baseline models. Notably, the PINN achieves up to a 97% reduction in current estimation errors while maintaining high voltage prediction accuracy. Extensive simulations further assess model performance under noisy inputs and partial observability, where the PINN consistently outperforms conventional data-driven approaches. These results highlight the method’s ability to generalize under uncertainty, accelerate convergence, and preserve physical consistency in simulated real-world conditions without requiring large volumes of labeled training data. Full article

Under the background of “dual-carbon”, expanding renewable energy grid integration exacerbates grid net load volatility, and system climbing requirements escalate. In this paper, the problem of uncertain ramping demand prediction caused by net load prediction error in new power systems is investigated. First, the total system ramping demand calculation model is constructed, and the effects of deterministic and uncertain ramping demand on the total system ramping demand are analyzed. Secondly, a prediction model based on a CNN-LSTM hybrid neural network is proposed for the uncertain ramp-up demand, which extracts the spatial correlation features of the multi-source influencing factors through the convolutional layer, captures the dynamic evolution law in the time series by using the LSTM layer, and realizes the high-precision point prediction and reliable interval prediction by combining the quantile regression method. Finally, the actual operation data and forecast data of a provincial power grid are used for example verification, and the results show that the proposed model outperformed traditional models (SVM, RF, BPNN) and single deep learning models (CNN, LSTM) in point prediction performance, achieving higher prediction accuracy and validating the effectiveness of the spatio-temporal feature extraction module. In terms of interval prediction quality, compared with the histogram and QRF benchmark models, the proposed model achieves a significant reduction in the average width of the prediction interval, average upward ramp-up demand, and average downward ramp-down demand while maintaining 100% interval coverage. This demand realizes a better balance between prediction economic efficiency and safety, providing more reliable technical support for the precise assessment of uncertain ramp-up demand in new power systems. Full article

A significant advancement in Neural Network (NN) research is the integration of domain-specific knowledge through custom loss functions. This approach addresses a crucial challenge: How can models utilize physics or mathematical principles to enhance predictions when dealing with sparse, noisy, or incomplete data? Physics-Informed Neural Networks (PINNs) put this idea into practice by incorporating a forward model, such as Partial Differential Equations (PDEs), as soft constraints. This guidance helps the networks find solutions that align with established laws. Recently, researchers have expanded this framework to include Bayesian NNs (BNNs) which allow for uncertainty quantification. However, what happens when the governing equations of a system are not completely known? In this work, we introduce methods to automatically select PDEs from historical data in a parametric family. We then integrate these learned equations into three different modeling approaches: PINNs, Bayesian-PINNs (B-PINNs), and Physical-Informed Bayesian Linear Regression (PI-BLR). To assess these frameworks, we evaluate them on a real-world Multivariate Time Series (MTS) dataset related to electrical power energy management. We compare their effectiveness in forecasting future states under different scenarios: with and without PDE constraints and accuracy considerations. This research aims to bridge the gap between data-driven discovery and physics-guided learning, providing valuable insights for practical applications. Full article

The so-called Holiness Code (Leviticus 17–26) describes the land (אֶרֶץ) almost as a human being. In biblical scholarship on this law-text, the land has often been explained as an independent agent and more powerful than even the human beings that are present in the text. This paper will use social network analysis to test these conclusions and provide a more detailed account of the role of the land. The paper sets out to develop a social network model of the Holiness Code by including all interactions among human/divine participants and physical space. The paper then explores how human/divine participants relate to space, and it is shown that the participant roles are closely connected to access to space. Afterwards, the social role of the land is scrutinized by exploring each of its relationships, and by conducting a cluster analysis to understand the structural properties of the network. It is shown that the land is not as central and agentive as is usually thought but, rather, that the land plays a secondary role as a vulnerable character in need of protection. The paper is concluded by reflections on the potential of social network analysis for understanding character roles in literature. Full article

Conventional integer-order models fail to adequately capture non-local memory effects and constrained nonlinear interactions in emotional dynamics. To address these limitations, we propose a coupled framework that integrates Caputo fractional derivatives with hyperbolic tangent–based interaction functions. The fractional-order term quantifies power-law memory decay in affective states, while the nonlinear component regulates connection strength through emotional difference thresholds. Mathematical analysis establishes the existence and uniqueness of solutions with continuous dependence on initial conditions and proves the local asymptotic stability of network equilibria ($W_{ij}^{*}={\displaystyle \frac{1}{\delta }}{\sec \mathrm{h}}^2(\parallel {\mathrm{E}}_i-{\mathrm{E}}_j\parallel )$, e.g., $W^{*}\approx 1.40$ under typical parameters $\eta =0.5$, $\delta =0.3$). We further derive closed-form expressions for the steady-state variance under stochastic perturbations ($\mathrm{Var}\left(W_{ij}\right)={\displaystyle \frac{{\sigma }_{\zeta }^2}{2\eta \delta }}$) and demonstrate a less than 6% deviation between simulated and theoretical values when ${\sigma }_{\zeta }=0.1$. Numerical experiments using the Euler–Maruyama method validate the convergence of connection weights toward the predicted equilibrium, reveal Gaussian features in the stationary distributions, and confirm power-law scaling between noise intensity and variance. The numerical accuracy of the fractional system is further verified through L1 discretization, with observed error convergence consistent with theoretical expectations for $\mu =0.5$. This framework advances the mechanistic understanding of co-evolutionary dynamics in emotion-modulated social networks, supporting applications in clinical intervention design, collective sentiment modeling, and psychophysiological coupling research. Full article

To address the interception problem against maneuvering targets, this paper proposes a multi-agent cooperative guidance law based on a multi-directional interception formation. A three-dimensional agent–target engagement kinematics model is established, and a fixed-time observer is designed to estimate the target acceleration. By utilizing the agent-to-agent communication network, real-time exchange of motion state information among the agents is realized. Based on this, a control input along the line-of-sight (LOS) direction is designed to directly regulate the agent–target relative velocity, effectively driving the agent swarm to achieve time-to-go consensus within a fixed-time boundary. Furthermore, adaptive variable-power sliding mode control inputs are designed for both elevation and azimuth angles. By adjusting the power of the control inputs according to a preset sliding threshold, the proposed method achieves fast convergence in the early phase and smooth tracking in the latter phase under varying engagement conditions. This ensures that the elevation and azimuth angles of each agent–target pair converge to the desired values within a fixed-time boundary, forming a multi-directional interception formation and significantly improving the interception performance against maneuvering targets. Simulation results demonstrate that the proposed cooperative guidance law exhibits fast convergence, strong robustness, and high accuracy. Full article

A graph’s minimum cycle basis is defined as the smallest collection of cycles that exhibit linear independence in the cycle space, serving as fundamental building blocks for constructing any cyclic structure within the graph. These bases are useful in various contexts, including the intricate analysis of electrical networks, structural engineering endeavors, chemical processes, and surface reconstruction techniques, etc. This study investigates the urban road networks of six Chinese cities to analyze their topological features, node centrality, and robustness (resilience to traffic disruptions) using motif analysis and minimum cycle bases methodologies. Some interesting conclusions are obtained: the frequency of motifs containing cycles exceeds that of random networks with equivalent degree sequences; the frequency distribution of minimum cycle lengths and surface areas obeys the power-law distribution. The cycle contribution rate is introduced to investigate the centrality of nodes within road networks, and has a significant impact on the total number of cycles in the robustness analysis. Finally, we construct two types of cycle-based dual networks for urban road networks by representing cycles as nodes and establishing edges between two cycles sharing a common node and edge, respectively. The results show that cycle-based dual networks exhibit small-world and scale-free properties. The research facilitates a comprehensive understanding of the cycle structure characteristics in urban road networks, thereby providing a theoretical foundation for both subsequent modeling endeavors of transportation networks and optimization strategies for existing road infrastructure. Full article

Reducing surface roughness can enhance the mechanical properties of processed materials. The variation law of the aluminum alloy surface roughness induced by continuous-nanosecond combined laser (CL) with different continuous laser power densities and laser delay is investigated experimentally. A back propagation neural network (BPNN) coupled with a sparrow search algorithm (SSA) is employed to predict surface roughness. The nanosecond laser energy density, continuous laser power density and laser delay are input parameters, while the surface roughness is output parameter. The lowest surface roughness is achieved with completely paint film removed by the CL while the nanosecond laser energy density is 1.99 J/cm², the continuous laser power density is 2118 W/cm² and the laser delay is 1 ms. Compared to the original target and the target irradiated by nanosecond pulse laser (ns laser), the reductions in the surface roughness are 20.62% and 12.00%, respectively. The SSA-BPNN model demonstrates high prediction accuracy, with a correlation coefficient (R²) of 0.98628, root mean square error (RMSE) of 0.024, mean absolute error (MAE) of 0.020 and mean absolute percentage error (MAPE) of 1.30% on the test set. These results indicate that the SSA-BPNN demonstrates higher-precision surface roughness prediction with limited experimental data than BPNN. Furthermore, the findings confirm that the CL can effectively reduce surface roughness. Full article

Physics-Informed Neural Networks (PINNs) have emerged as a powerful approach for integrating physical laws into a deep learning framework, offering enhanced capabilities for solving complex problems. Despite their potential, the practical implementation of PINNs presents significant challenges. This paper explores the application of PINNs to systems of ordinary differential equations (ODEs), focusing on two key challenges: the forward problem of solution approximation and the inverse problem of parameter estimation. We present three detailed case studies involving dynamical systems for tumor growth, gene expression, and the SIR (Susceptible, Infected, Recovered) model for disease spread. This paper outlines the core principles of PINNs and their integration with physical laws during neural network training. It details the steps involved in implementing PINNs, emphasizing the critical role of network architecture and hyperparameter tuning in achieving optimal performance. Additionally, we provide a Python package, ODE-PINN, to reproduce results for ODE-based systems. Our findings demonstrate that PINNs can yield accurate and consistent solutions, but their performance is highly sensitive to network architecture and hyperparameters tuning. These results underscore the need for customized configurations and robust optimization strategies. Overall, this study confirms the significant potential of PINNs to advance the understanding of dynamical systems in biology and epidemiology. Full article