A Deep Neural Network (DNN) based algorithm is proposed for the detection and classification of faults in industrial plants. The proposed algorithm has the ability to classify faults, especially incipient faults that are difficult to detect and diagnose with traditional threshold based statistical methods or by conventional Artificial Neural Networks (ANNs). The algorithm is based on a Supervised Deep Recurrent Autoencoder Neural Network (Supervised DRAE-NN) that uses dynamic information of the process along the time horizon. Based on this network a hierarchical structure is formulated by grouping faults based on their similarity into subsets of faults for detection and diagnosis. Further, an external pseudo-random binary signal (PRBS) is designed and injected into the system to identify incipient faults. The hierarchical structure based strategy improves the detection and classification accuracy significantly for both incipient and non-incipient faults. The proposed approach is tested on the benchmark Tennessee Eastman Process resulting in significant improvements in classification as compared to both multivariate linear model-based strategies and non-hierarchical nonlinear model-based strategies.

While photovoltaic (PV) systems are installed at an unprecedented rate, reliable information on an installation level remains scarce. As a result, automatically created PV registries are a timely contribution to optimize grid planning and operations. This paper demonstrates how aerial imagery and three-dimensional building data can be combined to create an address-level PV registry, specifying area, tilt, and orientation angles. We demonstrate the benefits of this approach for PV capacity estimation. In addition, this work presents, for the first time, a comparison between automated and officially-created PV registries. Our results indicate that our enriched automated registry proves to be useful to validate, update, and complement official registries.

Can we infer all the failed components of an infrastructure network, given a sample of reachable nodes from supply nodes? One of the most critical post-disruption processes after a natural disaster is to quickly determine the damage or failure states of critical infrastructure components. However, this is non-trivial, considering that often only a fraction of components may be accessible or observable after a disruptive event. Past work has looked into inferring failed components given point probes, i.e. with a direct sample of failed components. In contrast, we study the harder problem of inferring failed components given partial information of some `serviceable' reachable nodes and a small sample of point probes, being the first often more practical to obtain. We formulate this novel problem using the Minimum Description Length (MDL) principle, and then present a greedy algorithm that minimizes MDL cost effectively. We evaluate our algorithm on domain-expert simulations of real networks in the aftermath of an earthquake. Our algorithm successfully identify failed components, especially the critical ones affecting the overall system performance.

While there has been a surge of recent interest in learning differential equation models from time series, methods in this area typically cannot cope with highly noisy data. We break this problem into two parts: (i) approximating the unknown vector field (or right-hand side) of the differential equation, and (ii) dealing with noise. To deal with (i), we describe a neural network architecture consisting of tensor products of one-dimensional neural shape functions. For (ii), we propose an alternating minimization scheme that switches between vector field training and filtering steps, together with multiple trajectories of training data. We find that the neural shape function architecture retains the approximation properties of dense neural networks, enables effective computation of vector field error, and allows for graphical interpretability, all for data/systems in any finite dimension $d$. We also study the combination of either our neural shape function method or existing differential equation learning methods with alternating minimization and multiple trajectories. We find that retrofitting any learning method in this way boosts the method's robustness to noise. While in their raw form the methods struggle with 1% Gaussian noise, after retrofitting, they learn accurate vector fields from data with 10% Gaussian noise.

In this paper, we propose two nonparametric methods used in the forecasting of functional time-dependent data, namely functional singular spectrum analysis recurrent forecasting and vector forecasting. Both algorithms utilize the results of functional singular spectrum analysis and past observations in order to predict future data points where recurrent forecasting predicts one function at a time and the vector forecasting makes predictions using functional vectors. We compare our forecasting methods to a gold standard algorithm used in the prediction of functional, time-dependent data by way of simulation and real data and we find our techniques do better for periodic stochastic processes.

A new machine learning algorithm is poised to help urban transportation analysts relieve bottlenecks and chokepoints that routinely snarl city traffic. The tool, called TranSEC, was developed at the U.S. Department of Energy's Pacific Northwest National Laboratory to help urban traffic engineers get access to actionable information about traffic patterns in their cities. Currently, publicly available traffic information at the street level is sparse and incomplete. Traffic engineers generally have relied on isolated traffic counts, collision statistics and speed data to determine roadway conditions. The new tool uses traffic datasets collected from UBER drivers and other publicly available traffic sensor data to map street-level traffic flow over time.

Artificial intelligence-powered use cases for climate action could help organisations meet up to 45% of the Economic Emission Intensity (EEI) targets of the Paris Agreement. New research from the Capgemini Research Institute has found that while AI offers many climate action use cases, only 13% of organisations are successfully combining climate vision with AI capabilities. AI use cases include improving energy efficiency, reducing dependence on fossil fuels and optimising processes to aid productivity. The research found that 67% of organisations have long-term business goals to tackle climate change. While many technologies address a specific outcome, such as carbon capture or renewable sources of energy, AI can accelerate organisations' climate action across sectors and value chains.

Real time, accurate passive seismic event detection is a critical safety measure across a range of monitoring applications from reservoir stability to carbon storage to volcanic tremor detection. The most common detection procedure remains the Short-Term-Average to Long-Term-Average (STA/LTA) trigger despite its common pitfalls of requiring a signal-to-noise ratio greater than one and being highly sensitive to the trigger parameters. Whilst numerous alternatives have been proposed, they often are tailored to a specific monitoring setting and therefore cannot be globally applied, or they are too computationally expensive therefore cannot be run real time. This work introduces a deep learning approach to event detection that is an alternative to the STA/LTA trigger. A bi-directional, long-short-term memory, neural network is trained solely on synthetic traces. Evaluated on synthetic and field data, the neural network approach significantly outperforms the STA/LTA trigger both on the number of correctly detected arrivals as well as on reducing the number of falsely detected events. Its real time applicability is proven with 600 traces processed in real time on a single processing unit.

Google India on Thursday said artificial intelligence alone can add $500 billion to the economy, and assist in better forecast of floods and accurate diagnosis of diseases. The company has committed $10 billion for expanding India's digital footprint, a top official said. Google recently picked up a 7.73 percent stake in Reliance Industries Ltd's (RIL) digital subsidiary, Jio Platforms Ltd. The two companies have also announced plans to come up with "an entry-level affordable smartphone". Gupta said during the pandemic, data consumption jumped to 14 GB per month from 8 GB.

Nearly a decade after the three meltdowns at Fukushima No. 1 nuclear power plant, plans are underway to finally remove nuclear fuel debris from the three reactors. But in order to remove it, Tokyo Electric Power Company Holdings Inc. (Tepco), the operator of the plant, needs to ensure there is a place to store the debris once it is retrieved. This is thought to be the reason why the government is rushing to give the green light to releasing tritium-laced water piling up at the plant into the Pacific -- to give room for the storage of fuel debris. But removing the fuel debris -- a crucial step in the decommissioning process -- is an enormous task on its own, with measures that need to be resolved emerging one after another. At a three-day online meeting of the Atomic Energy Society of Japan from Sept. 16, an official from the International Research Institute for Nuclear Decommissioning (IRID) who is in charge of technical development regarding the decommissioning of the Fukushima plant, explained the plan, or the lack thereof, to remove the debris at reactor No. 2. "We will consider what kind of measures to take, comparing tactics and developing techniques," the official said, with a hint of frustration at not being able to come up with a specific way yet.