• A novel GNN-based surrogate model predicts material deformation with high accuracy. • Recurrent structures capture temporal dynamics in large material deformations. • Graph U-Net architecture efficiently processes large and complex geometries. • New contact representation method improves efficiency in large contact interactions. • The model facilitates manufacturability assessment in material forming. In recent years, various artificial intelligence-based surrogate models have been proposed to provide rapid manufacturability predictions of material forming processes. However, traditional AI-based surrogate models, typically built with scalar or image-based neural networks, are limited in their ability to capture complex 3D spatial relationships and to operate in a permutation-invariant manner. To overcome these issues, emerging graph-based surrogate models are developed using graph neural networks. This study developed a new graph neural network surrogate model named Recurrent U Net-based Graph Neural Network (RUGNN). The RUGNN model can achieve accurate predictions of sheet material deformation fields across multiple forming timesteps. The RUGNN model incorporates Gated Recurrent Units (GRUs) to model temporal dynamics and a U-Net inspired graph-based downsample/upsample mechanism to handle spatial long-range dependencies. A novel ’node-to-surface’ contact representation method was proposed, offering significant improvements in computational efficiency for large-scale contact interactions. The RUGNN model was validated using a cold forming case study and a more complex hot forming case study using aluminium alloys. Results demonstrate that the RUGNN model provides accurate deformation predictions closely matching ground truth FE simulations and outperforming several baseline GNN architectures. Model tuning was also performed to identify suitable hyperparameters, training strategies, and input feature representations. These results demonstrate that RUGNN is a reliable approach to support sheet material forming design by enabling accurate manufacturability predictions.

In multivariate time series (MTS) analysis, data loss is a critical issue that degrades analytical model performance and impairs downstream tasks such as structural health monitoring (SHM) and traffic flow monitoring. In real-world applications, MTS is usually collected by multiple types of sensors, making MTS and correlations between variates heterogeneous. However, existing MTS imputation methods overlook the heterogeneous correlations by manipulating heterogeneous MTS as a homogeneous entity, leading to inaccurate imputation results. Besides, correlations between different data types vary due to ever-changing environmental conditions, forming dynamic correlations in MTS. How to properly learn the hidden correlation from heterogeneous MTS for accurate data imputation remains unresolved. To solve the problem, we propose a multistage graph convolutional network with spatial attention (MSA-GCN). In the first stage, we decompose heterogeneous MTS into several clusters with homogeneous data collected from identical sensor types and learn intracluster correlations. Then, we devise a GCN with spatial attention to explore dynamic intercluster correlations, which is the second stage of MSA-GCN. In the last stage, we decode the learned features from previous stages via stacked convolutional neural networks. We jointly train these three-stage models to predict the missing data in MTS. Leveraging this multistage architecture and spatial attention mechanism makes MSA-GCN effectively learn heterogeneous and dynamic correlations among MTS, resulting in superior imputation performance. We tested MSA-GCN with the monitoring data from a large-span bridge and Wetterstation weather dataset. The results affirm its superiority over baseline models, demonstrating its enhanced accuracy in reducing imputation errors across diverse datasets.

Lightning protection near the fan is the key point of lightning protection in wind farm, which affects the safe and effective delivery of wind power resources. At present, the three-phase multi-gap arrester installed on a base tower behind the collector can cooperate with the zinc oxide arrester to reduce the system failure caused by the arrester failure. However, the chopped wave generated by the multi-gap arrester when it acts is transmitted to the fan box transformer along the line, which may threaten the insulation of the transformer winding. Based on the above problems, this paper builds a simulation model on ATP to analyze the impact of wave clipping of multi-gap arrester on the insulation of fan transformer windings, considers the conventional installation mode and the limit installation mode of multi-gap arrester, and calculates that the wave clipping overvoltage generated by multi-gap arrester after being struck by lightning in two cases is the impact on the insulation of fan transformer. Through the simulation analysis, it is found that the higher the amplitude of the lightning current, the larger the amplitude of the overvoltage generated by the truncated wave, the steeper the wave head, and the greater the threat to the insulation of the fan box. The damage degree of round lightning is less than that of counter lightning. When a multi-gap arrester is installed in a collector tower, the 100[Formula: see text]kA lightning current may destroy the insulation of the fan box transformer winding and require additional protective measures. It is recommended that the multi-gap arrester be installed from the next pole tower of the collector tower in the wind farm.

The emergence of the Internet of Things (IoT) has facilitated the development and usage of low-computational microcontrollers at the edge of the network, which process data in the proximity of data sources and thereby offload the pressure of data transmission. Recently, IoT is becoming a key technology for structural health monitoring (SHM) systems. This study designs a novel wireless IoT monitoring system for the Hong Kong–Zhuhai–Macao Bridge, the world longest sea-crossing bridge. The 5G technology and edge computing are integrated to improve the system performance in sensor serviceability, data transmission, time synchronization, and data quality control. The artificial intelligent (AI) algorithm is embedded into the NVIDIA Xavier NX edge computing boards to preliminarily detect data anomalies caused by sensor faults, before uploading the massive data to the cloud platform. As training AI models requires a large amount of labeled data and is always time consuming, a novel data anomaly detection method is developed by transferring the model trained from the other bridge to the target bridge. Given that prestoring source data in edge devices consumes expensive storage resources, the source-free domain adaptation is developed by integrating the robust self-training mechanism and self-knowledge distillation strategy. Thus, the model transfer is achieved cross bridges in the absence of source data. This study provides a valuable and practical reference for developing a wireless IoT SHM system for large-scale infrastructure and enabling edge computing for data anomaly detection with high efficiency and accuracy.

Online A/B tests have become increasingly popular and important for social platforms. However, accurately estimating the global average treatment effect (GATE) has proven to be challenging due to network interference, which violates the Stable Unit Treatment Value Assumption (SUTVA) and poses great challenge to experimental design. Existing network experimental design research was mostly based on the unbiased Horvitz-Thompson (HT) estimator with substantial data trimming to ensure unbiasedness at the price of high resultant estimation variance. In this paper, we strive to balance the bias and variance in designing randomized network experiments. Under a potential outcome model with 1-hop interference, we derive the bias and variance of the standard HT estimator and reveal their relation to the network topological structure and the covariance of the treatment assignment vector. We then propose to formulate the experimental design problem as to optimize the covariance matrix of the treatment assignment vector to achieve the bias and variance balance by minimizing the mean squared error (MSE) of the estimator. An efficient projected gradient descent algorithm is presented to the implement of the desired randomization scheme. Finally, we carry out extensive simulation studies to demonstrate the advantages of our proposed method over other existing methods in many settings, with different levels of model misspecification.

Estimating engineering structures’ health conditions and predicting their future behaviors are fundamental problems for a city’s safe and efficient operations. Data-driven solutions estimate the health conditions using statistical models generated from measurement data. They have attracted growing interest recently because advances in information and communication technologies (ICTs) have enabled numerous real-world measurement data, and the flourishing big data community has provided enormous state-of-the-art data analytics algorithms. Nevertheless, most existing studies remain in numerical simulation while neglecting their real-world implementation, restricting the extensive development of data-driven methods. In this survey, we provide a structural overview of the past decade’s data-driven structural health monitoring (SHM) systems and algorithms from the perspective of real-world implementation. Specifically, we cover various aspects of the design and implementation of monitoring systems, including sensing technologies, communication technologies, and processing software. In addition, we classify the used data sources and statistical models with fined details. Under the proposed taxonomy, their limitations and advantages are thoroughly discussed. Based on our insights into existing studies, we clarify two major implementation challenges: 1) the digressive performance in the real-world environment and 2) inefficient computing systems for real-time data analytics. Possible solutions are then proposed to mitigate those challenges and promote the implementation of data-driven methods. Finally, we raise our outlook on future trends and suggest promising directions for further investigation.

Trustworthy and real-time video surveillance aims to analyze the live camera streams in a privacy-preserving manner for the decision-making of various advanced services, such as pedestrian reidentification and traffic monitoring. In recent years, edge computing has been identified as a promising technology for trustworthy and real-time video surveillance because it keeps confidential video data locally and reduces the latency caused by massive data transmission. Generally, a single edge device can hardly afford the computation-intensive video analytics tasks. Most existing solutions incorporate cloud servers to handle the overloaded tasks. However, such an edge-cloud collaboration approach still suffers from unpredictable latency and privacy concerns because the remote cloud is centralized and distant from the cameras. In this work, we designed a blockchain-based collaborative edge intelligence (BCEI) approach for trustworthy and real-time video surveillance. In BCEI, geo-distributed edge devices form a peer-to-peer network to maintain a permissioned blockchain and share data and computation resources to perform computation-intensive video analytics tasks. The video analytics results are written on the blockchain in an immutable manner to guarantee trustworthiness. To reduce task execution time, we formulate and solve a joint stream mapping and task scheduling problem to schedule video streams and machine learning models among edge devices. A pedestrian reidentification prototype is implemented and deployed based on BCEI with the extensive performance evaluation, indicating the superiority of BCEI in latency reduction and system throughput improvement by leveraging collaboration among edge devices.

Traditional methods of learning conditional statements follow lecture-based instruction, which focuses on teaching concepts and definitions. While this traditional method of instruction is the dominant teaching style in Computer Science (CS) education, it has also been identified to be one of the least effective methods for teaching and engaging students [3]. The existing limitations of traditional methods include low engagement, disconnection from the real-world applications, a lack of valuable artifacts to evaluate the learning outcomes. To improve the learning methods of conditional statements stated above, we asked the following two questions: RQ1: Can storytelling logic, an early development logic, be a viable tool that translates the concept of condition followed by a consequence to the logic of conditional statements? RQ2: In what approaches can we apply the isomorphic relationship between the two logics to the learning of conditional statements?