Your data will never be shared with third parties, however, we may send you information from time to time about our products that may be of interest to you. By submitting your details, you agree to be contacted by us. You may contact us at any time to opt-out. Artificial Intelligence in Retail Market to 2025 - Global Analysis and Forecasts by Retail Format (E-Commerce and, Brick & Mortar), Technology (Chat-B.. Artificial Lift System Market Forecast to 2028 - Covid-19 Impact and Global Analysis - by Type (Progressive Cavity Pumping (PCP), Rod Lift, Gas Lift,..

Probabilistic graphical models have emerged as a powerful modeling tool for several real-world scenarios where one needs to reason under uncertainty. A graphical model's partition function is a central quantity of interest, and its computation is key to several probabilistic reasoning tasks. Given the #P-hardness of computing the partition function, several techniques have been proposed over the years with varying guarantees on the quality of estimates and their runtime behavior. This paper seeks to present a survey of 18 techniques and a rigorous empirical study of their behavior across an extensive set of benchmarks. Our empirical study draws up a surprising observation: exact techniques are as efficient as the approximate ones, and therefore, we conclude with an optimistic view of opportunities for the design of approximate techniques with enhanced scalability. Motivated by the observation of an order of magnitude difference between the Virtual Best Solver and the best performing tool, we envision an exciting line of research focused on the development of portfolio solvers.

Microgrids – small, decentralised power hubs that use local sources of energy – have long been touted as a solution to the problem of ageing national grids, which are becoming increasingly prone to cyber attacks, blackouts and inefficiencies where power is consumed far from where it is produced. Despite their potential, uptake has been patchy, so microgrids have yet to capture the public imagination. But AI technology is helping to turn them into viable hyper-local projects that can serve global carbon-reduction ambitions. Rotterdam is the location of Europe's largest seaport. Since August 2020, it has also been home to what's understood to be the world's first high-frequency, decentralised energy market, where port users share and sell clean energy.

Partial differential equations are often used to model various physical phenomena, such as heat diffusion, wave propagation, fluid dynamics, elasticity, electrodynamics and image processing, and many analytic approaches or traditional numerical methods have been developed and widely used for their solutions. Inspired by rapidly growing impact of deep learning on scientific and engineering research, in this paper we propose a novel neural network, GF-Net, for learning the Green's functions of linear reaction-diffusion equations in an unsupervised fashion. The proposed method overcomes the challenges for finding the Green's functions of the equations on arbitrary domains by utilizing physics-informed approach and the symmetry of the Green's function. As a consequence, it particularly leads to an efficient way for solving the target equations under different boundary conditions and sources. We also demonstrate the effectiveness of the proposed approach by experiments in square, annular and L-shape domains.

The Nearly Autonomous Management and Control System (NAMAC) is a comprehensive control system that assists plant operations by furnishing control recommendations to operators in a broad class of situations. This study refines a NAMAC system for making reasonable recommendations during complex loss-of-flow scenarios with a validated Experimental Breeder Reactor II simulator, digital twins improved by machine-learning algorithms, a multi-attribute decision-making scheme, and a discrepancy checker for identifying unexpected recommendation effects. We assessed the performance of each NAMAC component, while we demonstrated and evaluated the capability of NAMAC in a class of loss-of-flow scenarios.

The constant increase in the amount and complexity of information obtained from IT data networkelements, for its correct monitoring and management, is a reality. The same happens to data net-works in electrical systems that provide effective supervision and control of substations and hydro-electric plants. Contributing to this fact is the growing number of installations and new environmentsmonitored by such data networks and the constant evolution of the technologies involved. This sit-uation potentially leads to incomplete and/or contradictory data, issues that must be addressed inorder to maintain a good level of monitoring and, consequently, management of these systems. Inthis paper, a prototype of an expert system is developed to monitor the status of equipment of datanetworks in electrical systems, which deals with inconsistencies without trivialising the inferences.This is accomplished in the context of the remote control of hydroelectric plants and substationsby a Regional Operation Centre (ROC). The expert system is developed with algorithms definedupon a combination of Fuzzy logic and Paraconsistent Annotated Logic with Annotation of TwoValues (PAL2v) in order to analyse uncertain signals and generate the operating conditions (faulty,normal, unstable or inconsistent / indeterminate) of the equipment that are identified as importantfor the remote control of hydroelectric plants and substations. A prototype of this expert systemwas installed on a virtualised server with CLP500 software (from the EFACEC manufacturer) thatwas applied to investigate scenarios consisting of a Regional (Brazilian) Operation Centre, with aGeneric Substation and a Generic Hydroelectric Plant, representing a remote control environment.

A powerful once-in-a-decade winter storm in February resulted in the near total collapse of Texas' power grid, resulting in residential and commercial areas suffering days-long blackouts, which led to at least 57 deaths and billions of dollars in property damage across the state's 254 counties. In addition, some Texans who did have power are facing overcharges of about $16 billion for electricity consumed during the weeklong crisis, according to a watchdog for the Electric Reliability Council of Texas (ERCOT), the quasi-governmental entity that oversees the Lone Star State's power grid. While debates as to the root causes of the grid's failure are likely to go on for months if not years, some energy experts contend that a potential solution exists that could have alleviated some of the worst effects of the power shutdown – the introduction of artificial intelligence (AI) into the management of the grid. Artificial Intelligence is loosely defined as the use of computer systems to process large volumes of data in order to perform tasks that normally require human intelligence, such as visual perception, speech recognition and decision-making. Although AI technology has been embraced by a number of other economic sectors, such as retail and insurance industries, the operators of the U.S. power grid have been slower to adopt it.

Artificial Intelligence is turning out to be a game changer, with countless applications in nearly every domain. It is now making its way into the area of Production and Manufacturing, allowing it to harness the power of deep learning and in doing so, providing automation that is faster, cheaper and more superior. This article aims to give a brief understanding of automated visual assessment and how a deep learning approach can save significant time and effort. It involves the analysis of products on the production line for the purpose of quality control. Visual inspection can also be used for internal and external assessment of the various equipment in a production facility such as storage tanks, pressure vessels, piping, and other equipment.

Artificial Intelligence (AI) is becoming an integral part of the tech world. It is revolutionizing science, healthcare and our daily lives more than we would have imagined. From speech recognition and chatbots to self-driving cars, deep AI is playing a pivotal role. Although AI is the future of the world, training AI algorithms still depend on powerful computers, which consume significant energy and emit considerable carbon emissions. As AI evolves rapidly, so does the research about its carbon emissions.

The saturation of mobile devices and ubiquitous connectivity has steeped the world in an array of wireless connectivity, from the growing terrestrial and non-terrestrial cellular infrastructure and supporting fiber and wireless backhaul networks to the massive IoT ecosystem with newly developed protocols and SoCs to support the billions of sensor nodes intended to send data to the cloud. By 2025, the global datasphere is expected to approach 175 zettabytes per year. What's more, the number of connected devices is anticipated to reach 50 billion by 2030. However, the traditional distributed sensing scheme with the cloud-based centralized processing of data has severe limitations in security, power management, and latency -- the end-to-end (E2E) latencies for ultra-reliable low-latency communications found in 5G standards are on the order of tens of milliseconds. This has led to a demand to drive data processing to the edge, disaggregating computational (and storage) resources to reduce the massive overhead that comes with involving the entire signal chain in uplink and downlink transmissions. New advances in machine learning (ML) and deep neural networks (DNNs) with artificial intelligence promise to provide this insight at the edge, but these solutions come with a huge computational burden that cannot be satisfied with conventional software and embedded processor approaches.