The advances in deep learning (DL) techniques have the potential to deliver transformative technological breakthroughs to numerous complex tasks in modern power systems that suffer from increasing uncertainty and nonlinearity. However, the vulnerability of DL has yet to be thoroughly explored in power system tasks under various physical constraints. This work, for the first time, proposes a novel physics-constrained backdoor poisoning attack, which embeds the undetectable attack signal into the learned model and only performs the attack when it encounters the corresponding signal. The paper illustrates the proposed attack on the real-time fault line localization application. Furthermore, the simulation results on the 68-bus power system demonstrate that DL-based fault line localization methods are not robust to our proposed attack, indicating that backdoor poisoning attacks pose real threats to DL implementations in power systems. The proposed attack pipeline can be easily generalized to other power system tasks.

Additive Manufacturing (AM) is a powerful technology that produces complex 3D geometries using various materials in a layer-by-layer fashion. However, quality assurance is the main challenge in AM industry due to the possible time-varying processing conditions during AM process. Notably, new defects may occur during printing, which cannot be mitigated by offline analysis tools that focus on existing defects. This challenge motivates this work to develop online learning-based methods to deal with the new defects during printing. Since AM typically fabricates a small number of customized products, this paper aims to create an online learning-based strategy to mitigate the new defects in AM process while minimizing the number of samples needed. The proposed method is based on model-free Reinforcement Learning (RL). It is called Continual G-learning since it transfers several sources of prior knowledge to reduce the needed training samples in the AM process. Offline knowledge is obtained from literature, while online knowledge is learned during printing. The proposed method develops a new algorithm for learning the optimal defect mitigation strategies proven the best performance when utilizing both knowledge sources. Numerical and real-world case studies in a fused filament fabrication (FFF) platform are performed and demonstrate the effectiveness of the proposed method.

This paper introduces pmuGE (phasor measurement unit Generator of Events), one of the first data-driven generative model for power system event data. We have trained this model on thousands of actual events and created a dataset denoted pmuBAGE (the Benchmarking Assortment of Generated PMU Events). The dataset consists of almost 1000 instances of labeled event data to encourage benchmark evaluations on phasor measurement unit (PMU) data analytics. PMU data are challenging to obtain, especially those covering event periods. Nevertheless, power system problems have recently seen phenomenal advancements via data-driven machine learning solutions. A highly accessible standard benchmarking dataset would enable a drastic acceleration of the development of successful machine learning techniques in this field. We propose a novel learning method based on the Event Participation Decomposition of Power System Events, which makes it possible to learn a generative model of PMU data during system anomalies. The model can create highly realistic event data without compromising the differential privacy of the PMUs used to train it. The dataset is available online for any researcher or practitioner to use at the pmuBAGE Github Repository: https://github.com/NanpengYu/pmuBAGE.

Paul Brackman loads 3D-printed metal samples into a tower for examination using an X-ray CT scan in DOE's Manufacturing Demonstration Facility at ORNL.

The number of sequential tasks a single gripper can perform is significantly limited by its design. In many cases, changing the gripper fingers is required to successfully conduct multiple consecutive tasks. For this reason, several robotic tool change systems have been introduced that allow an automatic changing of the entire end-effector. However, many situations require only the modification or the change of the fingertip, making the exchange of the entire gripper uneconomic. In this paper, we introduce a paradigm for automatic task-specific fingertip production. The setup used in the proposed framework consists of a production and task execution unit, containing a robotic manipulator, and two 3D printers - autonomously producing the gripper fingers. It also consists of a second manipulator that uses a quick-exchange mechanism to pick up the printed fingertips and evaluates gripping performance. The setup is experimentally validated by conducting automatic production of three different fingertips and executing graspstability tests as well as multiple pick- and insertion tasks, with and without position offsets - using these fingertips. The proposed paradigm, indeed, goes beyond fingertip production and serves as a foundation for a fully automatic fingertip design, production and application pipeline - potentially improving manufacturing flexibility and representing a new production paradigm: tactile 3D manufacturing.

This paper presents a novel data-driven framework to aid in system state estimation when the power system is under unobservable false data injection attacks. The proposed framework dynamically detects and classifies false data injection attacks. Then, it retrieves the control signal using the acquired information. This process is accomplished in three main modules, with novel designs, for detection, classification, and control signal retrieval. The detection module monitors historical changes in phasor measurements and captures any deviation pattern caused by an attack on a complex plane. This approach can help to reveal characteristics of the attacks including the direction, magnitude, and ratio of the injected false data. Using this information, the signal retrieval module can easily recover the original control signal and remove the injected false data. Further information regarding the attack type can be obtained through the classifier module. The proposed ensemble learner is compatible with harsh learning conditions including the lack of labeled data, concept drift, concept evolution, recurring classes, and independence from external updates. The proposed novel classifier can dynamically learn from data and classify attacks under all these harsh learning conditions. The introduced framework is evaluated w.r.t. real-world data captured from the Central New York Power System. The obtained results indicate the efficacy and stability of the proposed framework.

In modern industrial systems, diagnosing faults in time and using the best methods becomes more and more crucial. It is possible to fail a system or to waste resources if faults are not detected or are detected late. Machine learning and deep learning have proposed various methods for data-based fault diagnosis, and we are looking for the most reliable and practical ones. This paper aims to develop a framework based on deep learning and reinforcement learning for fault detection. We can increase accuracy, overcome data imbalance, and better predict future defects by updating the reinforcement learning policy when new data is received. By implementing this method, we will see an increase of $3\%$ in all evaluation metrics, an improvement in prediction speed, and $3\%$ - $4\%$ in all evaluation metrics compared to typical backpropagation multi-layer neural network prediction with similar parameters.

In this context, additive manufacturing (AM), and specifically metal additive manufacturing (MAM), is particularly suited to industrial paradigms based on automation, flexibility, and efficiency. Indeed, MAM can be considered as a native digital technology, providing a seamless workflow from the digital design environment to the final product, which can be potentially completed without any human intervention [30]. However, a broader adoption of MAM technologies in industry is still hindered by such factors as: (i) lack of widely adopted standardisations and specifications of material properties, machines, and processes [40]; (ii) lack of adequate digital infrastructures, and interoperability issues between different production environments [7]; (iii) lack of accessible interfaces providing process information that is easily interpretable by non-experts [47]; (iv) lack of advanced control systems capable of automatically adjusting, at run-time, the production parameters [54]; (v) challenges in quality assurance due part accuracy and variability [48]. Thus, achieving semantically transparent and interoperable data sets and systems, to address Points (i), (ii) and (iii) above, is arguably of paramount importance. In this direction, several approaches based on ontology engineering and knowledge representation techniques have been proposed [29, 10, 66, 67, 60]. Broadly conceived as formal specifications of conceptualisations over a domain of interest, computational ontologies (cf.

Drones working together can create large 3D-printed structures made of foam or cement. The experiments are paving the way for a future where swarms of drones could help construct extremely tall or intricate buildings and other structures like bridges without the need for support scaffolding or large construction machinery. "We're talking about being able to build something of limitless size, theoretically speaking," says Robert Stuart-Smith at the University of Pennsylvania. Such creations would only be restricted by structural engineering constraints and factors like drone flight logistics. The drone swarm construction takes inspiration from animals such as wasps and termites.

Example image of the 3D printer nozzle used by the machine learning algorithm to detect and correct errors in real time. Engineers from the University of Cambridge have developed a machine learning algorithm that can detect and correct a wide variety of different errors in real time, and can be easily added to new or existing machines to enhance their capabilities. Details of their low-cost approach are reported in the journal Nature Communications. However, it is also vulnerable to production errors, from small-scale inaccuracies and mechanical weaknesses through to total build failures. Currently, the way to prevent or correct these errors is for a skilled worker to observe the process.