Resilient Control Systems
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Resilient Control Systems
In our modern society, computerized or digital control systems have been used to reliably automate many of the industrial operations that we take for granted, from the power plant to the automobiles we drive. However, the complexity of these systems and how the designers integrate them, the roles and responsibilities of the humans that interact with the systems, and the cyber security of these highly networked systems have led to a new paradigm in research philosophy for next-generation control systems. Resilient Control Systems consider all of these elements and those disciplines that contribute to a more effective design, such as cognitive psychology, computer science, and control engineering to develop interdisciplinary solutions. These solutions consider things such as how to tailor the control system operating displays to best enable the user to make an accurate and reproducible response, how to design in cybersecurity protections such that the system defends itself from attack ...
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Control System
A control system manages, commands, directs, or regulates the behavior of other devices or systems using control loops. It can range from a single home heating controller using a thermostat controlling a domestic boiler to large industrial control systems which are used for controlling processes or machines. The control systems are designed via control engineering process. For continuously modulated control, a feedback controller is used to automatically control a process or operation. The control system compares the value or status of the process variable (PV) being controlled with the desired value or setpoint (SP), and applies the difference as a control signal to bring the process variable output of the plant to the same value as the setpoint. For sequential and combinational logic, software logic, such as in a programmable logic controller, is used. Open-loop and closed-loop control There are two common classes of control action: open loop and closed loop. In an ...
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Control Flow
In computer science, control flow (or flow of control) is the order in which individual statements, instructions or function calls of an imperative program are executed or evaluated. The emphasis on explicit control flow distinguishes an ''imperative programming'' language from a '' declarative programming'' language. Within an imperative programming language, a ''control flow statement'' is a statement that results in a choice being made as to which of two or more paths to follow. For non-strict functional languages, functions and language constructs exist to achieve the same result, but they are usually not termed control flow statements. A set of statements is in turn generally structured as a block, which in addition to grouping, also defines a lexical scope. Interrupts and signals are low-level mechanisms that can alter the flow of control in a way similar to a subroutine, but usually occur as a response to some external stimulus or event (that can occur asynchronously), ...
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United States Computer Emergency Readiness Team
The United States Computer Emergency Readiness Team (US-CERT) is an organization within the Department of Homeland Security’s (DHS) Cybersecurity and Infrastructure Security Agency (CISA). Specifically, US-CERT is a branch of the Office of Cybersecurity and Communications' (CS&C) National Cybersecurity and Communications Integration Center (NCCIC). US-CERT is responsible for analyzing and reducing cyber threats, vulnerabilities, disseminating cyber threat warning information, and coordinating incident response activities. The division brings advanced network and digital media analysis expertise to bear on malicious activity targeting the networks within the United States and abroad. Background The concept of a national Computer Emergency Response Team (CERT) for the United States was proposed by Marcus Sachs (Auburn University) when he was a staff member for the U.S. National Security Council in 2002 to be a peer organization with other national CERTs such as AusCERT and C ...
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Human Factors And Ergonomics Society
The Human Factors and Ergonomics Society (HFES) is an interdisciplinary nonprofit professional organization, headquartered in Washington, D.C., United States, covering the fields of human factors and ergonomics. Founded in 1957, HFES claims 4500 members worldwide. HFES has 67 active chapters throughout the United States, Canada, and Europe. 42 of these are student chapters, and 23 are technical groups. HFES publishes the ANSI/HFES-100 standard for ergonomic workstation setups, approved by the American National Standards Institute The American National Standards Institute (ANSI ) is a private non-profit organization that oversees the development of voluntary consensus standards for products, services, processes, systems, and personnel in the United States. The organi ... (ANSI) in 2007. References External links {{authority control Professional associations based in the United States Art and design-related professional associations Architecture-related prof ...
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Human Reliability
Human reliability (also known as human performance or HU) is related to the field of human factors and ergonomics, and refers to the reliability of humans in fields including manufacturing, medicine and nuclear power. Human performance can be affected by many factors such as age, state of mind, physical health, attitude, emotions, propensity for certain common mistakes, errors and cognitive biases, etc. Human reliability is very important due to the contributions of humans to the resilience of systems and to possible adverse consequences of human errors or oversights, especially when the human is a crucial part of the large socio-technical systems as is common today. User-centered design and error-tolerant design are just two of many terms used to describe efforts to make technology better suited to operation by humans. Common Traps of Human Nature People tend to overestimate their ability to maintain control when they are doing work. The common characteristics of human nat ...
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Probabilistic Risk Assessment
Probabilistic risk assessment (PRA) is a systematic and comprehensive methodology to evaluate risks associated with a complex engineered technological entity (such as an airliner or a nuclear power plant) or the effects of stressors on the environment (probabilistic environmental risk assessment, or PERA). Risk in a PRA is defined as a feasible detrimental outcome of an activity or action. In a PRA, risk is characterized by two quantities: #the magnitude (severity) of the possible adverse consequence(s), and #the likelihood (probability) of occurrence of each consequence. Consequences are expressed numerically (e.g., the number of people potentially hurt or killed) and their likelihoods of occurrence are expressed as probabilities or frequencies (i.e., the number of occurrences or the probability of occurrence per unit time). The total risk is the expected loss: the sum of the products of the consequences multiplied by their probabilities. The spectrum of risks across classe ...
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Electroencephalography
Electroencephalography (EEG) is a method to record an electrogram of the spontaneous electrical activity of the brain. The biosignals detected by EEG have been shown to represent the postsynaptic potentials of pyramidal neurons in the neocortex and allocortex. It is typically non-invasive, with the EEG electrodes placed along the scalp (commonly called "scalp EEG") using the International 10-20 system, or variations of it. Electrocorticography, involving surgical placement of electrodes, is sometimes called " intracranial EEG". Clinical interpretation of EEG recordings is most often performed by visual inspection of the tracing or quantitative EEG analysis. Voltage fluctuations measured by the EEG bioamplifier and electrodes allow the evaluation of normal brain activity. As the electrical activity monitored by EEG originates in neurons in the underlying brain tissue, the recordings made by the electrodes on the surface of the scalp vary in accordance with their orientation and ...
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Bayesian Network
A Bayesian network (also known as a Bayes network, Bayes net, belief network, or decision network) is a probabilistic graphical model that represents a set of variables and their conditional dependencies via a directed acyclic graph (DAG). Bayesian networks are ideal for taking an event that occurred and predicting the likelihood that any one of several possible known causes was the contributing factor. For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. Efficient algorithms can perform inference and learning in Bayesian networks. Bayesian networks that model sequences of variables (''e.g.'' speech signals or protein sequences) are called dynamic Bayesian networks. Generalizations of Bayesian networks that can represent and solve decision problems under uncertainty are called influence diagrams. Graphical mode ...
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Bayesian Inference
Bayesian inference is a method of statistical inference in which Bayes' theorem is used to update the probability for a hypothesis as more evidence or information becomes available. Bayesian inference is an important technique in statistics, and especially in mathematical statistics. Bayesian updating is particularly important in the dynamic analysis of a sequence of data. Bayesian inference has found application in a wide range of activities, including science, engineering, philosophy, medicine, sport, and law. In the philosophy of decision theory, Bayesian inference is closely related to subjective probability, often called "Bayesian probability". Introduction to Bayes' rule Formal explanation Bayesian inference derives the posterior probability as a consequence of two antecedents: a prior probability and a "likelihood function" derived from a statistical model for the observed data. Bayesian inference computes the posterior probability according to Bayes' theorem: ...
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Human Behavior
Human behavior is the potential and expressed capacity ( mentally, physically, and socially) of human individuals or groups to respond to internal and external stimuli throughout their life. Kagan, Jerome, Marc H. Bornstein, and Richard M. Lerner.Human Behaviour." ''Encyclopædia Britannica''. 2020. Retrieved 5 June 2020. Behavior is driven by genetic and environmental factors that affect an individual. Behavior is also driven, in part, by thoughts and feelings, which provide insight into individual psyche, revealing such things as attitudes and values. Human behavior is shaped by psychological traits, as personality types vary from person to person, producing different actions and behavior. Social behavior accounts for actions directed at others. It is concerned with the considerable influence of social interaction and culture, as well as ethics, interpersonal relationships, politics, and conflict. Some behaviors are common while others are unusual. The acceptability of ...
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Multi-agent System
A multi-agent system (MAS or "self-organized system") is a computerized system composed of multiple interacting intelligent agents.Hu, J.; Bhowmick, P.; Jang, I.; Arvin, F.; Lanzon, A.,A Decentralized Cluster Formation Containment Framework for Multirobot Systems IEEE Transactions on Robotics, 2021. Multi-agent systems can solve problems that are difficult or impossible for an individual agent or a monolithic system to solve.Hu, J.; Turgut, A.; Lennox, B.; Arvin, F.,Robust Formation Coordination of Robot Swarms with Nonlinear Dynamics and Unknown Disturbances: Design and Experiments IEEE Transactions on Circuits and Systems II: Express Briefs, 2021. Intelligence may include methodic, functional, procedural approaches, algorithmic search or reinforcement learning.Hu, J.; Bhowmick, P.; Lanzon, A.,Group Coordinated Control of Networked Mobile Robots with Applications to Object Transportation IEEE Transactions on Vehicular Technology, 2021. Despite considerable overlap, a multi-ag ...
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State Space (controls)
In control engineering, a state-space representation is a mathematical model of a physical system as a set of input, output and state variables related by first-order differential equations or difference equations. State variables are variables whose values evolve over time in a way that depends on the values they have at any given time and on the externally imposed values of input variables. Output variables’ values depend on the values of the state variables. The "state space" is the Euclidean space in which the variables on the axes are the state variables. The state of the system can be represented as a ''state vector'' within that space. To abstract from the number of inputs, outputs and states, these variables are expressed as vectors. If the dynamical system is linear, time-invariant, and finite-dimensional, then the differential and algebraic equations may be written in matrix form. The state-space method is characterized by significant algebraization of general syste ...
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