268 research outputs found
Combining Multi-Agent Systems and Wireless Sensor Networks for Monitoring Crop Irrigation
[EN]Monitoring mechanisms that ensure efficient crop growth are essential on many farms,
especially in certain areas of the planet where water is scarce. Most farmers must assume the high
cost of the required equipment in order to be able to streamline natural resources on their farms.
Considering that many farmers cannot afford to install this equipment, it is necessary to look for more
effective solutions that would be cheaper to implement. The objective of this study is to build virtual
organizations of agents that can communicate between each other while monitoring crops. A low cost
sensor architecture allows farmers to monitor and optimize the growth of their crops by streamlining
the amount of resources the crops need at every moment. Since the hardware has limited processing
and communication capabilities, our approach uses the PANGEA architecture to overcome this
limitation. Specifically, we will design a system that is capable of collecting heterogeneous information
from its environment, using sensors for temperature, solar radiation, humidity, pH, moisture and
wind. A major outcome of our approach is that our solution is able to merge heterogeneous data from
sensors and produce a response adapted to the context. In order to validate the proposed system, we
present a case study in which farmers are provided with a tool that allows us to monitor the condition
of crops on a TV screen using a low cost device.European Commision (EC). Funding H2020/MSCARISE. Project Code: 641794European Commision (EC). Funding FP7/SPE/SME. Project Code: 283638European Commision (EC). Funding FP7/SP1/ENV. Project Code: 28294
Multi-Agent Systems
A multi-agent system (MAS) is a system composed of multiple interacting intelligent agents. Multi-agent systems can be used to solve problems which are difficult or impossible for an individual agent or monolithic system to solve. Agent systems are open and extensible systems that allow for the deployment of autonomous and proactive software components. Multi-agent systems have been brought up and used in several application domains
Computational intelligence approaches to robotics, automation, and control [Volume guest editors]
No abstract available
An Industrial Data Analysis and Supervision Framework for Predictive Manufacturing Systems
Due to the advancements in the Information and Communication Technologies field in the
modern interconnected world, the manufacturing industry is becoming a more and more
data rich environment, with large volumes of data being generated on a daily basis, thus
presenting a new set of opportunities to be explored towards improving the efficiency and
quality of production processes.
This can be done through the development of the so called Predictive Manufacturing
Systems. These systems aim to improve manufacturing processes through a combination
of concepts such as Cyber-Physical Production Systems, Machine Learning and real-time
Data Analytics in order to predict future states and events in production. This can be used
in a wide array of applications, including predictive maintenance policies, improving quality
control through the early detection of faults and defects or optimize energy consumption,
to name a few.
Therefore, the research efforts presented in this document focus on the design and development
of a generic framework to guide the implementation of predictive manufacturing
systems through a set of common requirements and components. This approach aims
to enable manufacturers to extract, analyse, interpret and transform their data into actionable
knowledge that can be leveraged into a business advantage. To this end a list
of goals, functional and non-functional requirements is defined for these systems based
on a thorough literature review and empirical knowledge. Subsequently the Intelligent
Data Analysis and Real-Time Supervision (IDARTS) framework is proposed, along with
a detailed description of each of its main components.
Finally, a pilot implementation is presented for each of this components, followed by the
demonstration of the proposed framework in three different scenarios including several use
cases in varied real-world industrial areas. In this way the proposed work aims to provide
a common foundation for the full realization of Predictive Manufacturing Systems
“Voltage control on active networks under adverse conditions.”
Due to the inclusion of new loads and the predominant increase in electricity demand associated with the limitations of new environmental projects to minimize carbon emissions, such as pollution resulting from the energy generated by fossil fuels, the incorporation of electrical systems with distributed generation attributes to the energy planning, plans greater efficiency for various sectors of energy consumer groups worldwide.
To maintain the effectiveness and reliable operation of the entire power system interconnected between grids and intelligent microgrids of electricity supply, standards must follow the established voltage levels in all terminals of the electrical power supply equipment supply, keeping them within limits. Both power utilities and distributed generation and consumers maintain the required design specifications for a reliable range of variation. The need to maintain a standardized voltage level is summed up in the treatment of possible failures that can occur when there is a voltage level acting beyond the limits established in extended equipment operating times.
Due to the failure to maintain constant voltage levels along the electrical power grids several voltage control methods are applied, mainly controlling absorption, production and reactive power flow at all levels of the system, as well as when adverse system conditions where levels can achieve loss of system stability and voltage collapse.
This research aims to characterize the appropriate methods for voltage correction and stability in active electrical networks under the influence of adverse conditions, whether natural influences or disasters, to influences related to the conditions of electrical energy systems, such as contingencies and distortions in other factors of the system that influence the level of voltage, to which some scientific publications relate [1-4], analyzing in an equationally calculated experimental way and simulations in MATLAB and ATPDRAW to prove the results.Agência
EmC-ICDSST 2019: 5th International Conference on Decision Support System Technology - ICDSST 2019 & EURO Mini Conference 2019 on "Decision Support Systems: Main Developments & Future Trends"
info:eu-repo/semantics/publishedVersio
Advances in Reinforcement Learning
Reinforcement Learning (RL) is a very dynamic area in terms of theory and application. This book brings together many different aspects of the current research on several fields associated to RL which has been growing rapidly, producing a wide variety of learning algorithms for different applications. Based on 24 Chapters, it covers a very broad variety of topics in RL and their application in autonomous systems. A set of chapters in this book provide a general overview of RL while other chapters focus mostly on the applications of RL paradigms: Game Theory, Multi-Agent Theory, Robotic, Networking Technologies, Vehicular Navigation, Medicine and Industrial Logistic
Supply Chain
Traditionally supply chain management has meant factories, assembly lines, warehouses, transportation vehicles, and time sheets. Modern supply chain management is a highly complex, multidimensional problem set with virtually endless number of variables for optimization. An Internet enabled supply chain may have just-in-time delivery, precise inventory visibility, and up-to-the-minute distribution-tracking capabilities. Technology advances have enabled supply chains to become strategic weapons that can help avoid disasters, lower costs, and make money. From internal enterprise processes to external business transactions with suppliers, transporters, channels and end-users marks the wide range of challenges researchers have to handle. The aim of this book is at revealing and illustrating this diversity in terms of scientific and theoretical fundamentals, prevailing concepts as well as current practical applications
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