Forecasting user-level multi-energy demand under data scarcity is challenging due to the strong spatial interdependencies among users and the complex coupling across heterogeneous energy carriers. To address the challenges, this paper proposes a few-shot forecasting approach for integrated electricity-gas-heat systems, enabling reliable prediction with limited historical data. First, a meta-learning graph neural network is developed to extract generalizable energy conversion features adaptively across user patterns, effectively alleviating data insufficiency. Second, to accurately capture system dynamics, a multi-relational spatiotemporal graph module is designed to hierarchically model intra-energy dynamics and inter-energy coupling, achieving adaptive fusion of heterogeneous spatiotemporal features. Furthermore, a knowledge-enhanced training mechanism is introduced by embedding physical energy conversion laws into meta-training via pseudo-tasks, enhancing physical consistency and preventing overfitting in few-shot adaptation. Extensive experiments on multi-energy datasets ultimately demonstrate that compared to state-of-the-art forecasting models, the proposed approach achieves significant improvements in average metrics 3.80% for MAE, 7.89% for RMSE, and 13.12% for MAPE).

The scarcity of aging data and the inconsistency of aging trends during the second life lithium-ion batteries (SL-LIBs) echelon utilization process make state-of-health (SOH) estimation challenging, hindering accurate monitoring when deployed as energy storage in power systems. To address this issue, a cross-domain physical information network (CDPIN) method is proposed to estimate SOH of SL-LIBs. First, an internal parameter inference module is proposed to address the difficulty in directly and accurately measuring the internal state information of SL-LIBs, supervising inference of aging parameters with discretized degradation physical mechanism. Second, a memory-assisted domain alignment mechanism is proposed to address feature distribution differences caused by inconsistent aging trends. It enhances the unified representation capability of aging trends through the best aging feature replay strategy. Further, a framework for fine-tuning the SOH estimation model is proposed to address the distribution shift between SL-LIBs and the source domain, dynamically adjusting parameters through degradation process modeling feedback. CDPIN utilizes widely available first life lithium-ion battery data as the source domain, overcoming the limitation of SL-LIBs data scarcity for SOH monitoring during echelon utilization. Finally, the effectiveness of the proposed method is verified using actual aging data and a semiphysical hardware platform.

Transient stability assessment is critical for supporting grid operator decisions and ensuring the secure operation of power systems. However, the high-dimensional complexity of system modeling and the limitations of binary classification hinder accurate evaluation. To address this, a physics-informed graph neural network is proposed to generate stagewise probabilistic trajectories of stability without relying on explicit prior models. First, a physics-graph collaborative framework is developed by embedding the swing equation to estimate rotor angles and suppress misjudgment. Second, within this framework, a distribution-aware aggregation module is proposed. It captures global power distribution and local state variations through recursive neighborhood updates and second-order pooling. Furthermore, to integrate physical laws with domain supervision and enhance physical consistency, a multiobjective interaction mechanism is designed, overcoming the limitations of purely data-driven methods in feature disentanglement. The proposed approach is validated on the IEEE 39-bus and IEEE 145-bus systems, demonstrating superior accuracy and effectiveness in comparison with baseline methods.

To improve the positioning accuracy and robustness of autonomous vehicles under the influence of non-Gaussian noise at a lower cost, this article proposes DeepGINS, a bidirectional long–short-term memory (Bi-LSTM)-based inertial navigation system (INS)/global navigation satellite system (GNSS) fusion methodology. This is the first work to combine Bi-LSTM-predicted IMU data with real-time IMU via virtual IMU (VIMU). First, a deep IMU network is trained to predict fine inertial measurements from the raw gyroscope and accelerometer outputs. These predictions are then fused with real-time IMU data through a VIMU architecture to minimize the limitations of deep learning predictions and the inability to efficiently obtain low-frequency signals. Second, an adaptive weighted average filter with dynamic parameter tuning is developed to minimize noise contamination during the VIMU data fusion process. Finally, an adaptive maximum entropy criterion iterative error state Kalman filter (AMC-IESKF) containing a Gaussian kernel function is designed to deal with nonlinear non-Gaussian noise in heterogeneous sensor fusion. The experimental results show that the proposed method improves the latitude estimation accuracy by 70% and the longitude estimation accuracy by 88% compared with the PreVINS method under non-Gaussian noise interference. In addition, the lateral position estimation accuracy of the proposed method is improved by about 58% compared to the Bi-LSTM method under GNSS rejection environment, which is more suitable for low-cost vehicle application environments.

In the Industrial Internet of Things (IIoT), data loss may occur in edge devices due to network latency, communication failures, and other factors. Therefore, a mask asymmetric transformer generative adversarial network (MATGAN) is proposed for imputing missing data at edge devices closer to the data source. First, an online collaborative architecture based on generative adversarial networks is proposed, progressively enhancing resolution and reducing embedding dimensions through a hierarchical structure, effectively mitigating excessive memory overhead. Then, to reduce initial computational costs, an asymmetric lightweight masked autoencoder is designed to achieve sparse sampling by randomly masking edge data, reducing the initial computational cost and learning the reconstruction of spatiotemporal patches. Moreover, a dynamic weighted loss is proposed, which assigns weights based on the difficulty of distinguishing patch imputation, and minimizing multi-scale similarity from easy to hard, thereby improving the recovery capability of complex textures and sharp edge regions. Experimental results demonstrate that the proposed imputation method effectively recovers data and reduces imputation errors and transmission latency.

A leak fault is a safety risk to interrupt the operation of the transportation process for intelligent automation system. To judge the system failure, a fault diagnosis method based on mixed neural networks is proposed in this paper. First, the multi-attribute extraction module is proposed to provide the local and global data changes caused by the leak event for the following neural networks, which reduces the noise influence from the complex environment. Second, a feature-sharing neural network is proposed to capture the multi-scale features of data changes and further implement leak fault detection and location through the sequence information and the corresponding sequential dependencies. Third, the task-dependency loss function is proposed to update the network parameters of the joint learning of different network outputs in the whole training process. Based on the proposed network structure, the proposed method could achieve two different targets of fault detection and location simultaneously for long-time series data. Finally, different case studies of the collected acoustic data are studied, and the analysis results show the effectiveness of the proposed method for leak fault detection.Note to Practitioners—A leak event is considered a sudden system fault in the transportation-oriented intelligent automation system. In order to reduce and minimize the system damage influence, leak fault diagnosis is the key point to identify operation risks based on the analysis result of the collected data changes. Thus, a mixed neural networks-based method is proposed to achieve the detection and location of leak events in this paper. With the combination of different neural network structures, multi-scale data changes could enhance the analysis capability of the proposed method for different fault diagnosis targets in complex operation scenarios. The case results show that the proposed method is an implementation way to ensure system safety and is better than other detection methods through the comparisons of different evaluation metrics.

In traditional leak location methods, the position of the leak point is located through the time difference of pressure change points of both ends of the pipeline. The inaccurate estimation of pressure change points leads to the wrong leak location result. To address it, adaptive dynamic programming is proposed to solve the pipeline leak location problem in this article. First, a pipeline model is proposed to describe the pressure change along pipeline, which is utilized to reflect the iterative situation of the logarithmic form of pressure change. Then, under the Bellman optimality principle, a value iteration (VI) scheme is proposed to provide the optimal sequence of the nominal parameter and obtain the pipeline leak point. Furthermore, neural networks are built as the VI scheme structure to ensure the iterative performance of the proposed method. By transforming into the dynamic optimization problem, the proposed method adopts the estimation of the logarithmic form of pressure changes of both ends of the pipeline to locate the leak point, which avoids the wrong results caused by unclear pressure change points. Thus, it could be applied for real-time leak location of long-distance pipeline. Finally, the experiment cases are given to illustrate the effectiveness of the proposed method.

Since most of the existing models based on the microgrids (MGs) are nonlinear, which could cause the controller oscillate, resulting in the excessive line loss, and the nonlinear could also lead to the controller design difficulty of MGs system. Therefore, this article researches the distributed voltage recovery consensus optimal control problem for the nonlinear MGs system with N -distributed generations (DGs), in the case of providing stringent real power sharing. First, based on the distributed cooperative control concept of multiagent systems and the critic neural networks (NNs), a novel distributed secondary voltage recovery consensus optimal control protocol is constructed via applying the backstepping technique and nonzero-sum (NZS) differential game strategy to realize the voltage recovery of island MGs. Meanwhile, the model identifier is established to reconstruct the unknown NZS games systems based on a three-layer NN. Then, a critic NN weight adaptive adjustment tuning law is proposed to ensure the convergence of the cost functions and the stability of the closed-loop system. Furthermore, according to Lyapunov stability theory, it is proven that all signals are uniform ultimate boundedness in the closed loop system and the voltage recovery synchronization error converges to an arbitrarily small neighborhood of the origin near. Finally, some simulation results in MATLAB illustrate the validity of the proposed control strategy.

In the real-time status monitoring of pipeline network, incomplete pressure data are unavoidable due to some device or communication errors. To solve this problem, a hierarchical data recovery method based on generative adversarial networks (GANs) is proposed in this article. First, a hierarchical data recovery framework is proposed to handle different numbers of incomplete data due to the structure of the semicentral pipeline network. Second, a joint attention module is presented to capture both interior nature and correlation relationships of multivariate pressure series and further guarantee the consistency of pressure data. Third, the macromicrodual discriminators are proposed to evaluate the recovery result through the combination of the local and global variation in temporal and spatial dependencies. Based on the novel structures, the proposed model is able to recover incomplete data with abnormal fluctuation values, unreasonable fixed values, or missing values. Finally, under a series of data recovery experiments, the efficiency of the proposed method is evaluated. Experimental results demonstrate that the proposed method is a practical way to ensure data recovery performance in the pipeline network.Note to Practitioners—Status monitoring based on pressure data is of great importance for safe and efficient operation in a pipeline network. However, due to unexpected situations, the appearance of incomplete pressure data affects the subsequent data processing and status analysis, resulting in an incorrect decision. In this article, a deep learning-based method is proposed to recover the incomplete data. With the help of the spatiotemporal dependencies of multivariate pressure series, the proposed method can recover different numbers of incomplete data through the no-missing part of pressure data. The experiment results show that the proposed method is better than the similar data recovery methods through three different evaluation metrics. In the future, we will address the data recovery problem without the complete data pairs in the training process.

In daily pipeline inspection, it is significant to ensure good network communication and security. With the development of drone technology, it is possible to apply drones as air routers to collect information from pipeline networks and transmit it to pipeline inspectors. It is also crucial to achieve optimal drone deployment in pipeline networks. This article proposes a two-phase evolution optimal 3-D drone layout algorithm to deploy drones in pipeline networks. First, a 3-D pipeline graph model is designed to represent the possible projection position of drones, and the objective function is proposed for optimal drone deployment. Then, in the first phase, based on the features of the 3-D pipeline graph, the drone flight rules and constraint conditions are presented to calculate the number of drones and the initial layout sequence. In the second phase, according to the objective function and the above results, every drone is continuously moved in a small area to achieve a tradeoff between signal coverage and interference. Moreover, the key parameters of the objective function can be discussed to further optimize drone deployment. Simulation results are presented to illustrate the effectiveness and advantages of the proposed algorithm.

In terms of pipeline leak detection, the unavoidable fact is that existing data could not provide enough effective leak data to train a high accuracy model. To address this issue, this article proposes mixed generative adversarial networks (mixed-GANs) as a practical way to provide additional data, ensuring data reliability. First, multitype generative networks with heterogeneous parameter-updating mechanisms are designed to explore a variety of different solutions and eliminate the potential risks of instable training and scenario collapse. Then, based on expert experience, two data constraints are proposed to describe leak characteristics and further evaluate the quality of generated leak data in the training process. Through integrating the particle swarm optimization algorithm into generative model training, mixed-GAN has better generation performance than the conventional gradient descent algorithm. Based on the above-mentioned contents, the proposed model is able to provide satisfactory leak data with different scenarios, contributing to data quantity expansion, data credibility enhancement, and data variety enrichment. Finally, extensive experiments are given to illustrate the effectiveness of the proposed generative model for pipeline network leak detection.

This paper investigates the situation awareness issue of power system with massive measured data. To address this issue, first, a graph-theory-based network partitioning algorithm is proposed to realize decentralized detection in a faster response speed, while using power flow characteristics highlights the independency of different groups. Further, a hierarchical event detection method is proposed to judge voltage change and locate event position according to spectral distribution change of established multidimensional matrix. With the proposed method, the system situation can be assessed and the knowledge of the system model is not required. In addition, the accurate result of weak event happened in system could also be obtained. The simulation results are presented to illustrate the effectiveness of the proposed detection method.