As safety is the top priority in mission-critical engineering applications, uncertainty quantification emerges as a linchpin to the successful deployment of AI models in these high-stakes domains. In this paper, we seamlessly encode a simple and principled uncertainty quantification module Spectral-normalized Neural Gaussian Process (SNGP) into GoogLeNet to detect various defects in steel wire ropes (SWRs) accurately and reliably. To this end, the developed methodology consists of three coherent steps. In the first step, raw Magnetic Flux Leakage (MFL) signals in waveform associated with normal and defective SWRs that are manifested in the number of broken wires are collected via a dedicated experimental setup. Next, the proposed approach utilizes Gramian Angular Field to represent the MFL signal in 1-D time series as 2-D images while preserving key spatial and temporal structures in the data. Thirdly, built atop the backbone of GoogLeNet, we systematically integrate SNGP by adding the spectral normalization (SN) layer to normalize the weights and replacing the output layers with a Gaussian process (GP) in the main network and auxiliary classifiers of GoogLeNet accordingly, where SN enables to preserve the distance in data transformation and GP makes the output layer of neural network distance aware when assigning uncertainty. Comprehensive comparisons with the state-of-the-art models highlight the advantages of the developed methodology in classifying SWR defects and identifying out-of-distribution (OOD) SWR instances. In addition, a thorough ablation study is performed to quantitatively illustrate the significant role played by SN and GP in the principledness of the estimated uncertainty towards detecting SWR instances with varying OODness.

Graph neural networks (GNNs) have transformed network analysis, leading to state-of-the-art performance across a variety of tasks. Especially, GNNs are increasingly been employed as detection tools in the AIoT environment in various security applications. However, GNNs have also been shown vulnerable to adversarial graph perturbation. We present the first approach for certifying robustness of general GNNs against attacks that add or remove graph edges either at training or prediction time. Extensive experiments demonstrate that our approach significantly outperforms prior art in certified robust predictions. In addition, we show that a non-certified adaptation of our method exhibits significantly better robust accuracy against state-of-the-art attacks that past approaches. Thus, we achieve both the best certified bounds and best practical robustness of GNNs to structural attacks to date.

Selective laser melting (SLM) is a commonly used technique in additive manufacturing to produce metal components with complex geometries and high precision. However, the poor process reproducibility and unstable product reliability has hindered its wide adoption in practice. Hence, there is a pressing demand for in-situ quality monitoring and real-time process control. In this paper, a feature-level multi-sensor fusion approach is proposed to combine acoustic emission signals with photodiode signals to realize in-situ quality monitoring for intelligence-driven production of SLM. An off-axial in-situ monitoring system featuring a microphone and a photodiode is developed to capture the process signatures during the building process. According to the 2D porosity and 3D density measurements, the collected acoustic and optical signals are grouped into three categories to indicate the quality of the produced parts. In consideration of the laser scanning information, an approach to transform the 1D signal to 2D image is developed. The converted images are then used to train a convolutional neural network so as to extract and fuse the features derived from the two individual sensors. In comparison with several baseline models, the proposed multi-sensor fusion approach achieves the best performance in quality monitoring.

As an emerging technology in the era of Industry 4.0, digital twin is gaining unprecedented attention because of its promise to further optimize process design, quality control, health monitoring, decision and policy making, and more, by comprehensively modeling the physical world as a group of interconnected digital models. In a two-part series of papers, we examine the fundamental role of different modeling techniques, twinning enabling technologies, and uncertainty quantification and optimization methods commonly used in digital twins. This second paper presents a literature review of key enabling technologies of digital twins, with an emphasis on uncertainty quantification, optimization methods, open source datasets and tools, major findings, challenges, and future directions. Discussions focus on current methods of uncertainty quantification and optimization and how they are applied in different dimensions of a digital twin. Additionally, this paper presents a case study where a battery digital twin is constructed and tested to illustrate some of the modeling and twinning methods reviewed in this two-part review. Code and preprocessed data for generating all the results and figures presented in the case study are available on GitHub.

Collisions during airport surface operations can create risk of injury to passengers, crew or airport personnel and damage to aircraft and ground equipment. A machine learning model that is able to predict the trajectories of ground objects can help to diminish the occurrences of such collision events. In this paper, we pursue this objective by building a spatial-temporal graph convolutional neural network (STG-CNN) model to predict the movement of objects/vehicles on the airport surface. The methodology adopted in this paper consists of three steps: (1) Raw data processing: leverage Apache Spark to parse a large volume of raw data in Flight Information Exchange Model (FIXM) format streamed from the Surface Movement Event Service (SMES) for the purpose of deriving historical trajectory associated with each object on the ground; (2.1) Graph-based representations of ground object movements: build graph-based representations to characterize the movements of ground objects over time, where graph edges are used capture the spatial relationships of ground objects with each other explicitly; (2.2) Trajectory forecasts of all ground objects: combine STG-CNN with Time-Extrapolator Convolution Neural Network (TXP-CNN) to forecast the future trajectories of all the ground objects as a whole; and (3) Separation distance-based safety assessment: define a probabilistic separation distance-based metric to assess the safety of airport surface movements. The performance of the developed model for trajectory prediction of ground objects is validated at two airports with varying scales: Hartsfield-Jackson Atlanta International Airport and LaGuardia airport, under two different scenarios (peak hour and off-peak hour). Two quantitative performance metrics — Average Displacement Error (ADE) and Final Displacement Error (FDE) are used to compare the prediction performance of the proposed model with an alternative method. The computational results indicate that the developed method has an ADE within the range [7.55, 9.33], and it significantly outperforms an alternative approach that combines a STG-CNN with Convolutional Long Short-Term Memory (ConvLSTM) neural network with an ADE of [15.79, 16.89] in airport surface movement prediction, thus facilitating more accurate safety assessment during airport surface operations.