Intelligent Control System Design
Control system appears in many systems e.g. our home, in car, industry and etc. Most of the processes will fail if the control system does not work properly. A control system manages, commands direct, or regulates the behavior of other devices or systems using control loops. It is highly important for the design of experimental equipment and instrumentation.
Machine Learning and Its Applications
Machine learning (ML) is a category of algorithm that allows software applications to become more accurate in predicting outcomes without being explicitly programmed. The basic premise of machine learning is to build algorithms that can receive input data and use statistical analysis to predict an output while updating outputs as new data becomes available. Things like growing volumes and varieties of available data, computational processing that is cheaper and more powerful, and affordable data storage are the main motivations toward machine learning developments.
AI for Security of Smart Cyber-physical System
The deployment of smart technologies in communication layer brings new challenges for online monitoring and control of the Cyber-Physical Systems (CPS). In addition to the failure of physical infrastructure, CPS is also sensitive to cyber-attacks on its communication layer. Examples of CPS include smart grid, autonomous transportation systems, medical monitoring, process control systems, robotics systems, and automatic pilot avionics. There are lots of discussions about the role of security-aware design and analysis in the development of modern CPS such as smart grid using advanced AI and machine learning techniques. AI is a popular ledger technology has the potential to be leveraged in different aspects of cyber security, cyber threat hunting, and cyber threat intelligence.
Smart Grid Analysis
The term “smart grid” in the context of power systems refer to a modernized electricity generation, transmission and distribution infrastructure. A smart grid can be described as a power system having bidirectional communications facilitated through the use of advanced sensing/metering devices and advanced control technologies.
Why the future belongs to the Smart Grid?
Demand for electricity is expected to grow 30% by 2035 as a result of new consumption models like smart plug-in vehicles, and smart homes. Traditional grid is not scalable enough to provide the world’s future energy requirements. Looking at the big picture, a nationwide effort to completely automate the grid is underway. Smart grid integrates distributed and renewable energy resources which results in more reliable transmission and distribution and expanded consumer end-uses.