The widespread adoption of autonomous vehicles (AVs) seems inevitable. Despite various concerns, AVs' development and implementation continues to advance. How will their spread affect sustainability? How will they affect humans' capacity to live on Earth in a way that does not threaten the planet's life-support function? We asked experts the following question: "How will the proliferation of autonomous vehicles affect sustainability?"

In this paper, we show the implementation of deep neural networks applied in process control. In our approach, we based the training of the neural network on model predictive control. Model predictive control is popular for its ability to be tuned by the weighting matrices and by the fact that it respects the constraints. We present the neural network that can approximate the behavior of the MPC in the way of mimicking the control input trajectory while the constraints on states and control input remain unimpaired of the value of the weighting matrices. This approach is demonstrated in a simulation case study involving a continuous stirred tank reactor, where multi-component chemical reaction takes place.

T. L. Carroll US Naval Research Lab, Washington, DC 20375 (Dated: December 16, 2019) A reservoir computer is a complex dynamical system, often created by coupling nonlinear nodes in a network. The nodes are all driven by a common driving signal. In this work, three dimension estimation methods, false nearest neighbor, covariance and Kaplan-Yorke dimensions, are used to estimate the dimension of the reservoir dynamical system. It is shown that the signals in the reservoir system exist on a relatively low dimensional surface. Changing the spectral radius of the reservoir network can increase the fractal dimension of the reservoir signals, leading to an increase in testing error. A reservoir computer uses a complex dynamical system to perform computations. The reservoir is often created by coupling together a set of nonlinear nodes. Each node is driven by a common input signal. The time series responses from each node are then used to fit a training signal that is related to the input. The training can take place via a least squares fit, while, the connections between nodes are not altered during training, so training a reservoir computer is fast. Reservoir computers are described as "high dimensional" dynamical systems because they contain many signals, but the concept of dimension is rarely explored. A reservoir with M nodes defines an M dimensional space, but the actual signals in the reservoir may live on a lower dimensional surface. Two different dimension estimation methods are used to find the dimension of this surface. Counterintuitively, as the dimension of this surface increases, the fits to the training signal become worse. The increase in reservoir dimension can be explained by a well known effect in driven dynamical systems that causes signals in the driven system to have a higher fractal dimension than the driving signal.

Despite their importance, such theorems offer no explanation for the advantages of neural networks, let alone deep neural networks, over classical approximation methods, since universal approximation properties are enjoyed by polynomials (Cheney and Light, 2009) as well as single layer neural networks (Cybenko, 1989). To address this, a recent thread has emerged in the literature concerning optimal approximation with deep ReLU networks, where the error in an optimal choice of weights and biases is bounded from above using the width and depth of the neural network. For example, using the "sawtooth" function of Telgarsky (2015), Y arotsky (2017) constructed an exponentially accurate (in the number of layers) ReLU network emulator for multiplication (x,y) null xy . This construction is used to obtain upper bounds on optimal approximation based upon DNN emulation of polynomial approximation. Building on these ideas, Opschoor et al. (2019) proved that optimal approximation with deep ReLU networks can emulate adaptive hp-finite element approximation, with greater depth allowing p -refinement to obtain exponential convergence rates. An additional contribution by He et al. (2018) reinterpreted single hidden layer ReLU networks as r -adaptive piecewise linear finite element spaces.

This paper proposes an Adaptive Stochastic Model Predictive Control (MPC) strategy for stable linear time-invariant systems in the presence of bounded disturbances. We consider multi-input, multi-output systems that can be expressed by a Finite Impulse Response (FIR) model. The parameters of the FIR model corresponding to each output are unknown but assumed sparse. We estimate these parameters using the Recursive Least Squares algorithm. The estimates are then improved using set-based bounds obtained by solving the Basis Pursuit Denoising [1] problem. Our approach is able to handle hard input constraints and probabilistic output constraints. Using tools from distributionally robust optimization, we reformulate the probabilistic output constraints as tractable convex second-order cone constraints, which enables us to pose our MPC design task as a convex optimization problem. The efficacy of the developed algorithm is highlighted with a thorough numerical example, where we demonstrate performance gain over the counterpart algorithm of [2], which does not utilize the sparsity information of the system impulse response parameters during control design.

Driven by climatic processes, wind power generation is inherently variable. Long-term simulated wind power time series are therefore an essential component for understanding the temporal availability of wind power and its integration into future renewable energy systems. In the recent past, mainly power curve based models such as Renewables.ninja (RN) have been used for deriving synthetic time series for wind power generation despite their need for accurate location information as well as for bias correction, and their insufficient replication of extreme events and short-term power ramps. We assess how time series generated by machine learning models (MLM) compare to RN in terms of their ability to replicate the characteristics of observed nationally aggregated wind power generation for Germany. Hence, we apply neural networks to one MERRA2 reanalysis wind speed input dataset with no location information and one with basic location information. The resulting time series and the RN time series are compared with actual generation. Both MLM time series feature equal or even better time series quality than RN depending on the characteristics considered. We conclude that MLM models can, even when reducing information on turbine locations and turbine types, produce time series of at least equal quality to RN.

Over the past few years, the manufacturing industry continued to remain a critical force in both advanced and developing economies. The sector has gone through significant transformations bringing out new opportunities and challenges to business leaders and policy makers. Get PDF Sample Copy of this report at https://decisionmarketreports.com/request-sample/1247548 In advanced economies, the manufacturing sector has largely concentrated on promoting innovation, productivity and trade more than growth and employment. In many advanced economies manufacturing sector has to consume more services and rely heavily on them to operate.

Forecasting of Energy Supply performs a vital role in the electric industry, as it gives the basis for giving decisions in power system planning and operation. A numerous variety of techniques for predicting power demand are being used by electrical firms, which are appropriate to short-term, medium-term or long-term forecasting. In such a changing environment common forecasting techniques are not sufficient, and more advanced methods are required. The aim is to completely untangle all the circumstances that lead to demand change and to discover the underlying problems. But analyzing many personal and social factors is difficult.

Multi-agent reinforcement learning (MARL) has long been a significant and everlasting research topic in both machine learning and control. With the recent development of (single-agent) deep RL, there is a resurgence of interests in developing new MARL algorithms, especially those that are backed by theoretical analysis. In this paper, we review some recent advances a sub-area of this topic: decentralized MARL with networked agents. Specifically, multiple agents perform sequential decision-making in a common environment, without the coordination of any central controller. Instead, the agents are allowed to exchange information with their neighbors over a communication network. Such a setting finds broad applications in the control and operation of robots, unmanned vehicles, mobile sensor networks, and smart grid. This review is built upon several our research endeavors in this direction, together with some progresses made by other researchers along the line. We hope this review to inspire the devotion of more research efforts to this exciting yet challenging area.

In a move that may forever change the face of education, Reliance Foundation's Jio Institute this week announced that they are launching graduate courses in artificial intelligence, data sciences, and digital media and integrated marketing communications for its first academic year by 2021. Earlier this year, Reliance Industries Ltd had informed the government's Empowered Expert Committee (EEC) that they were investing Rs 1,500 crore in Jio Institute, in the next two years to ensure that it creates a world-class centre of learning. Jio Insitute is reportedly going to build a 40,000 square foot edifice in Navi Mumbai for the same. Jio has revolutionised the Indian telecom sector by ushering in the age of latest data-centric technologies and propelled India into global digital leadership.