An Introduction to MultiAgent Systems.

As the demand for wind energy continues to grow at exponential rates, reducing operation and maintenance (OM) costs and improving reliability have become top priorities in wind turbine (WT) maintenance strategies. In addition to the development of more highly evolved WT designs intended to improve availability, the application of reliable and cost-effective condition-monitoring (CM) techniques offers an efficient approach to achieve this goal. This paper provides a general review and classification of wind turbine condition monitoring (WTCM) methods and techniques with a focus on trends and future challenges. After highlighting the relevant CM, diagnosis, and maintenance analysis, this work outlines the relationship between these concepts and related theories, and examines new trends and future challenges in the WTCM industry. Interesting insights from this research are used to point out strengths and weaknesses in today’s WTCM industry and define research priorities needed for the industry to meet the challenges in wind industry technological evolution and market growth.

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Wind Turbine Condition Monitoring: State-of-the-Art Review, New Trends, and Future Challenges

More particularly, calculations are developed and examined to reduce the entire quantity of Wireless access points as well as their locations in almost any given atmosphere while with the throughput needs and the necessity to ensure every place in the area can achieve a minimum of k APs. This paper concentrates on using Wireless for interacting with and localizing the robot. We've carried out thorough studies of Wireless signal propagation qualities both in indoor and outside conditions, which forms the foundation for Wireless AP deployment and communication to be able to augment how human operators communicate with this atmosphere, a mobile automatic platform is developed. Gas and oil refineries could be a harmful atmosphere for various reasons, including heat, toxic gasses, and unpredicted catastrophic failures. When multiple Wireless APs are close together, there's a possible for interference. A graph-coloring heuristic can be used to find out AP funnel allocation. Additionally, Wireless fingerprinting based localization is developed. All of the calculations implemented are examined in real life situations using the robot developed and answers are promising. For example, within the gas and oil industry, during inspection, maintenance, or repair of facilities inside a refinery, people might be uncovered to seriously high temps to have a long time, to toxic gasses including methane and H2S, and also to unpredicted catastrophic failures.

/pdf/internet-of-vehicles-from-intelligent-grid-to-autonomous-pma0tn3ofd.pdf

Internet of Vehicles: From Intelligent Grid to Autonomous Cars and Vehicular Clouds

Thank you for downloading some basic problems of the mathematical theory of elasticity. As you may know, people have search hundreds times for their favorite readings like this some basic problems of the mathematical theory of elasticity, but end up in infectious downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they cope with some infectious bugs inside their computer.

Some Basic Problems Of The Mathematical Theory Of Elasticity

Towards a semantic Construction Digital Twin: directions for future research

Decentralized fault detection and isolation in wireless structural health monitoring systems using analytical redundancy

IFC Monitor – An IFC schema extension for modeling structural health monitoring systems

Recent developments in artificial intelligence (AI), in particular machine learning (ML), have been significantly advancing smart city applications. Smart infrastructure, which is an essential component of smart cities, is equipped with wireless sensor networks that autonomously collect, analyze, and communicate structural data, referred to as “smart monitoring”. AI algorithms provide abilities to process large amounts of data and to detect patterns and features that would remain undetected using traditional approaches. Despite these capabilities, the application of AI algorithms to smart monitoring is still limited due to mistrust expressed by engineers towards the generally opaque AI inner processes. To enhance confidence in AI, the “black-box” nature of AI algorithms for smart monitoring needs to be explained to the engineers, resulting in so-called “explainable artificial intelligence” (XAI). However, when aiming at improving the explainability of AI algorithms through XAI for smart monitoring, the variety of AI algorithms requires proper categorization. Therefore, this review paper first identifies objectives of smart monitoring, serving as a basis to categorize AI algorithms or, more precisely, ML algorithms for smart monitoring. ML algorithms for smart monitoring are then reviewed and categorized. As a result, an overview of ML algorithms used for smart monitoring is presented, providing an overview of categories of ML algorithms for smart monitoring that may be modified to achieve explainable artificial intelligence in civil engineering.

Artificial Intelligence Techniques for Smart City Applications

A metamodel for cyber-physical systems

Artificial Intelligence (AI) has a long history in computer science and is now being applied to engineering problems in Structural Health Monitoring (SHM) that would be difficult to solve by standard numerical techniques alone. In particular, the methods of Conventional Artificial Intelligence (CAI) and Computational Intelligence (CI), coupled with agent technology, show great promise in delivering monitoring systems that are robust, redundant, environmentally aware, economically sound as well as user friendly and highly adaptive. In this paper, background concepts of AI and an example of a SHM system for monitoring civil engineering structures are presented to clearly demonstrate the potential of intelligent software applications in the field of SHM.

Kay Smarsly

Papers

Decentralized fault detection and isolation in wireless structural health monitoring systems using analytical redundancy

IFC Monitor – An IFC schema extension for modeling structural health monitoring systems

Artificial Intelligence Techniques for Smart City Applications

A metamodel for cyber-physical systems

Structural Health Monitoring based on Artificial Intelligence Techniques