A highway is closed due to snow and ice in Houston, Texas on Feb. 15, 2021. Up to 2.5 million ... [ ] customers were without power as the state's power generation capacity was impacted by an ongoing winter storm brought by Arctic blast. A new weather tech startup says it has created a new artificial intelligence (AI)-powered weather radar and satellite network to take on big weather. Climavision, which has $100 million in private equity funding, has created a high-resolution weather radar and satellite network that combines lower altitude, proprietary data with machine learning and AI technology. Chris Goode, CEO of Climavision, says the new sensing network will fill the coverage gaps in the existing NOAA and NWS systems across the US.

Model Predictive Control (MPC) is a versatile and a widely used for model-based control approaches, which involves an online optimization of the control strategy over a pre-determined predictive receding horizon. A central limitation of the traditional MPC online optimization is that it requires a relatively inexpensive models. As a result, linear and non-linear (quadratic) approximations to the plant-models are considered - unless, of-course an explicit model in the form of a differential equation is readily available. The non-linear modeling presents a computation challenge, that requires one to solve nonlinear programming problems online. This works fine for relatively low-dimensional systems.

Artificial Intelligence or simply AI is the science of designing intelligent computer programs or machines. AI will change the world as we know it by making everyday tasks easier and more efficient. AI is already created by major developers like IBM but has not nearly reached its full potential. Regardless of the benefits of AI there are many concerns with what the creation of AI can lead to, some as drastic as humanity creating their own uncontrollable superiors to even a third World War. Artificial Intelligence has been an enduring concept since the fifties when Arthur Samuel created the first computer program that taught itself how to play checkers in 1952.

Abstract-- This paper addresses task-allocation problems with uncertainty in situational awareness for distributed autonomous robots (DARs). The uncertainty propagation over a task-allocation process is done by using the Unscented transform that uses the Sigma-Point sampling mechanism. It has great potential to be employed for generic task-allocation schemes, in the sense that there is no need to modify an existing task-allocation method that has been developed without considering the uncertainty in the situational awareness. The proposed framework was tested in a simulated environment where the decision-maker needs to determine an optimal allocation of multiple locations assigned to multiple mobile flying robots whose locations come as random variables of known mean and covariance. The simulation result shows that the proposed stochastic task allocation approach generates an assignment with 30% less overall cost than the one without considering the uncertainty.

The multi-period dynamics of energy storage (ES), intermittent renewable generation and uncontrollable power loads, make the optimization of power system operation (PSO) challenging. A multi-period optimal PSO under uncertainty is formulated using the chance-constrained optimization (CCO) modeling paradigm, where the constraints include the nonlinear energy storage and AC power flow models. Based on the emerging scenario optimization method which does not rely on pre-known probability distribution functions, this paper develops a novel solution method for this challenging CCO problem. The proposed meth-od is computationally effective for mainly two reasons. First, the original AC power flow constraints are approximated by a set of learning-assisted quadratic convex inequalities based on a generalized least absolute shrinkage and selection operator. Second, considering the physical patterns of data and motived by learning-based sampling, the strategic sampling method is developed to significantly reduce the required number of scenarios through different sampling strategies. The simulation results on IEEE standard systems indicate that 1) the proposed strategic sampling significantly improves the computational efficiency of the scenario-based approach for solving the chance-constrained optimal PSO problem, 2) the data-driven convex approximation of power flow can be promising alternatives of nonlinear and nonconvex AC power flow.

Over the last few months here at Carnegie Mellon University (Australia campus) I've been giving a set of talks on AI and the great leaps it has made in the last 5 or so years. I focus on disruptive technologies and give examples ranging from smart fridges and jackets to autonomous cars, robots, and drones. The title of one of my talks is "AI and the 4th Industrial Revolution". Indeed, we are living in the 4th industrial revolution – a significant time in the history of mankind. The first revolution occurred in the 18th century with the advent of mechanisation and steam power; the second came about 100 years later with the discovery of electrical energy (among other things); and the big one, the 3rd industrial revolution, occurred another 100 years after that (roughly around the 1970s) with things like nuclear energy, space expeditions, electronics, telecommunications, etc. coming to the fore. So, yes, we are living in a significant time.

Geometrically parametrized Partial Differential Equations are nowadays widely used in many different fields as, for example, shape optimization processes or patient specific surgery studies. The focus of this work is on some advances for this topic, capable of increasing the accuracy with respect to previous approaches while relying on a high cost-benefit ratio performance. The main scope of this paper is the introduction of a new technique mixing up a classical Galerkin-projection approach together with a data-driven method to obtain a versatile and accurate algorithm for the resolution of geometrically parametrized incompressible turbulent Navier-Stokes problems. The effectiveness of this procedure is demonstrated on two different test cases: a classical academic back step problem and a shape deformation Ahmed body application. The results show into details the properties of the architecture we developed while exposing possible future perspectives for this work.

Backpropagation (BP) is a core component of the contemporary deep learning incarnation of neural networks. Briefly, BP is an algorithm that exploits the computational architecture of neural networks to efficiently evaluate the gradient of a cost function during neural network parameter optimization. The validity of BP rests on the application of a multivariate chain rule to the computational architecture of neural networks and their associated objective functions. Introductions to deep learning theory commonly present the computational architecture of neural networks in matrix form, but eschew a parallel formulation and justification of BP in the framework of matrix differential calculus. This entails several drawbacks for the theory and didactics of deep learning. In this work, we overcome these limitations by providing a full induction proof of the BP algorithm in matrix notation. Specifically, we situate the BP algorithm in the framework of matrix differential calculus, encompass affine-linear potential functions, prove the validity of the BP algorithm in inductive form, and exemplify the implementation of the matrix form BP algorithm in computer code.

In Internet of things (IoT), data is continuously recorded from different data sources and devices can suffer faults in their embedded electronics, thus leading to a high-dimensional data sets and concept drift events. Therefore, methods that are capable of high-dimensional non-stationary time series are of great value in IoT applications. Fuzzy Time Series (FTS) models stand out as data-driven non-parametric models of easy implementation and high accuracy. Unfortunately, FTS encounters difficulties when dealing with data sets of many variables and scenarios with concept drift. We present a new approach to handle high-dimensional non-stationary time series, by projecting the original high-dimensional data into a low dimensional embedding space and using FTS approach. Combining these techniques enables a better representation of the complex content of non-stationary multivariate time series and accurate forecasts. Our model is able to explain 98% of the variance and reach 11.52% of RMSE, 2.68% of MAE and 2.91% of MAPE.

Offshore wind power has fast become one of the most promising renewable sources of energy. Its growth is expected to continue, with generation capacity predicted to soar from 35GW to 234GW over the next 10 years, according to the Global Wind Energy Council (GWEC), which ranks the UK, Germany, and China as the largest national markets. The sector is a particular focus in governments' energy strategies, given the plummeting costs and the fact turbines can now be placed ever further from coastlines. Boris Johnson has even stated that he wants the UK to become the'Saudi Arabia of wind power'. The GWEC predicts significant growth over the next five years, with an estimated compound annual growth rate of nearly 32 percent, compared to just 0.3 percent with land-based turbines.