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Showing 20 of 626 academic articles

Distributed Systems

John A. Hamilton, David A. Nash, Udo W. Pooch • CRC Press eBooks (2020)

Multiple processor systems may be divided into two general categories: parallel processors (multiprocessors) and distributed systems (multicomputers). The distributed simulations, that is, simulations implemented over distributed systems. P...

OPENALEX DOI: https://doi.org/10.1201/9781003067849-5

A Behavioral SIQR-B Model for Cholera Transmission: Impact of Adaptive Risk Perception on Disease Dynamics

Pedro Pablo Cárdenas Alzate, Jaime Alberto Echeverri Franco, Jacobo Echeverri Hoyos • Epidemiologia (2026)

Background/Objectives: Cholera remains a major public health problem, particularly in regions with limited access to safe drinking water and sanitation. Traditional compartmental models may underrepresent adaptive human behavioral responses...

DOAJ Open Access (DOAJ) DOI: 10.3390/epidemiologia7050142

Regional observability of distributed systems

M. Amouroux, A. El Jai, El Hassan Zerrik • International Journal of Systems Science (1994)

The aim of this paper is to develop the concept of regional observability for distributed systems. This original concept is closer to practical considerations since it only requires the observation of the state on a given subregion. We show...

OPENALEX DOI: https://doi.org/10.1080/00207729408928961

The protoscience of cybersecurity

Eric Hatleback • The Journal of Defense Modeling and Simulation Applications Methodology Technology (2017)

I argue that marking a proper distinction between two types of research in the cybersecurity field obviates the present debate concerning a “science of cybersecurity.” Once the terminology has been properly disambiguated, the accurate descr...

OPENALEX cc-by-nc DOI: https://doi.org/10.1177/1548512917737635

Machine learning in cybersecurity: a comprehensive survey

Dipankar Dasgupta, Zahid Akhtar, Sajib Sen • The Journal of Defense Modeling and Simulation Applications Methodology Technology (2020)

Today’s world is highly network interconnected owing to the pervasiveness of small personal devices (e.g., smartphones) as well as large computing devices or services (e.g., cloud computing or online banking), and thereby each passing minut...

OPENALEX DOI: https://doi.org/10.1177/1548512920951275

Anomaly Detection for Cybersecurity of the Substations

Chee‐Wooi Ten, Junho Hong, Chen‐Ching Liu • IEEE Transactions on Smart Grid (2011)

Cybersecurity of the substations in a power system is a major issue as the substations become increasingly dependent on computer and communication networks. This paper is concerned with anomaly detection in the computer network environment ...

OPENALEX DOI: https://doi.org/10.1109/tsg.2011.2159406

Vulnerability Assessment of Cybersecurity for SCADA Systems

Chee‐Wooi Ten, Chen‐Ching Liu, Manimaran Govindarasu • IEEE Transactions on Power Systems (2008)

Vulnerability assessment is a requirement of NERC's cybersecurity standards for electric power systems. The purpose is to study the impact of a cyber attack on supervisory control and data acquisition (SCADA) systems. Compliance of the requ...

OPENALEX DOI: https://doi.org/10.1109/tpwrs.2008.2002298

Hacking the Human: The Prevalence Paradox in Cybersecurity

Ben D. Sawyer, Peter A. Hancock • Human Factors The Journal of the Human Factors and Ergonomics Society (2018)

OBJECTIVE: This work assesses the efficacy of the "prevalence effect" as a form of cyberattack in human-automation teaming, using an email task. BACKGROUND: Under the prevalence effect, rare signals are more difficult to detect, even when t...

OPENALEX Open Access DOI: https://doi.org/10.1177/0018720818780472

Cybersecurity in Hospitals: A Systematic, Organizational Perspective

Mohammad S. Jalali, Jessica Kaiser • Journal of Medical Internet Research (2018)

BACKGROUND: Cybersecurity incidents are a growing threat to the health care industry in general and hospitals in particular. The health care industry has lagged behind other industries in protecting its main stakeholder (ie, patients), and ...

OPENALEX cc-by DOI: https://doi.org/10.2139/ssrn.3100364 PMC: 5996174

Neural networks in control systems

Kumpati S. Narendra, Snehasis Mukhopadhyay • IEEE Conference on Decision and Control (CDC) (2005)

Some of the problems that arise in the control of nonlinear systems in the presence of uncertainty are considered. Multilayer neural networks and radial basis function networks are used in the design of identifiers and controllers, and grad...

OPENALEX DOI: https://doi.org/10.1109/cdc.1992.371803

Artificial neural networks in process engineering

M.J. Willis, Christine Di Massimo, Gary A. Montague, Ming T. Tham et al. • IEE Proceedings D Control Theory and Applications (1991)

Artificial neural networks are made up of highly interconnected layers of simple ‘neuron-like’ nodes. The neurons act as nonlinear processing elements within the network. An attractive property of artificial neural networks is that, given t...

OPENALEX DOI: https://doi.org/10.1049/ip-d.1991.0036

Optimization using neural networks

Gene A. Tagliarini, John Fury Christ, Edward W. Page • IEEE Transactions on Computers (1991)

The design of feedback (or recurrent) neural networks to produce good solutions to complex optimization problems is discussed. The theoretical basis for applying neural networks to optimization problems is reviewed, and a design rule that s...

OPENALEX DOI: https://doi.org/10.1109/12.106220

Neural networks for system identification

S. Reynold Chu, Rahmat A. Shoureshi, Manoel Fernando Tenorio • IEEE Control Systems Magazine (1990)

Two approaches are presented for utilization of neural networks in identification of dynamical systems. In the first approach, a Hopfield network is used to implement a least-squares estimation for time-varying and time-invariant systems. T...

OPENALEX DOI: https://doi.org/10.1109/37.55121

Nonlinear Flight Control Using Neural Networks

Byoung S. Kim, Anthony J. Calise • Journal of Guidance Control and Dynamics (1997)

The theoretical developmentofa direct adaptivetracking controlarchitectureusingneuralnetworks ispresented. Emphasis is placed on utilization of neural networks in a  ight control architecture based on feedback linearization of the aircraft...

OPENALEX DOI: https://doi.org/10.2514/2.4029

Learning and Evolution in Neural Networks

Stefano Nolfi, Domenico Parisi, Jeffrey L. Elman • Adaptive Behavior (1994)

This article describes simulations on populations of neural networks that both evolve at the population level and learn at the individual level. Unlike other simulations, the evolutionary task (finding food in the environment) and the learn...

OPENALEX DOI: https://doi.org/10.1177/105971239400300102

Modular learning in neural networks

Dana H. Ballard • National Conference on Artificial Intelligence (1987)

In the development of large-scale knowledge networks much recent progress has been inspired by connections to neurobiology. An important component of any neural network is an accompanying learning algorithm. Such an algorithm, to be biologi...

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