585 research outputs found
Optimization-Based Evolutionary Data Mining Techniques for Structural Health Monitoring
In recent years, data mining technology has been employed to solve various Structural Health Monitoring (SHM) problems as a comprehensive strategy because of its computational capability. Optimization is one the most important functions in Data mining. In an engineering optimization problem, it is not easy to find an exact solution. In this regard, evolutionary techniques have been applied as a part of procedure of achieving the exact solution. Therefore, various metaheuristic algorithms have been developed to solve a variety of engineering optimization problems in SHM. This study presents the most applicable as well as effective evolutionary techniques used in structural damage identification. To this end, a brief overview of metaheuristic techniques is discussed in this paper. Then the most applicable optimization-based algorithms in structural damage identification are presented, i.e. Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Imperialist Competitive Algorithm (ICA) and Ant Colony Optimization (ACO). Some related examples are also detailed in order to indicate the efficiency of these algorithms
Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration versus Algorithmic Behavior, Critical Analysis and Recommendations
In recent years, a great variety of nature- and bio-inspired algorithms has
been reported in the literature. This algorithmic family simulates different
biological processes observed in Nature in order to efficiently address complex
optimization problems. In the last years the number of bio-inspired
optimization approaches in literature has grown considerably, reaching
unprecedented levels that dark the future prospects of this field of research.
This paper addresses this problem by proposing two comprehensive,
principle-based taxonomies that allow researchers to organize existing and
future algorithmic developments into well-defined categories, considering two
different criteria: the source of inspiration and the behavior of each
algorithm. Using these taxonomies we review more than three hundred
publications dealing with nature-inspired and bio-inspired algorithms, and
proposals falling within each of these categories are examined, leading to a
critical summary of design trends and similarities between them, and the
identification of the most similar classical algorithm for each reviewed paper.
From our analysis we conclude that a poor relationship is often found between
the natural inspiration of an algorithm and its behavior. Furthermore,
similarities in terms of behavior between different algorithms are greater than
what is claimed in their public disclosure: specifically, we show that more
than one-third of the reviewed bio-inspired solvers are versions of classical
algorithms. Grounded on the conclusions of our critical analysis, we give
several recommendations and points of improvement for better methodological
practices in this active and growing research field.Comment: 76 pages, 6 figure
Optimal allocation of renewable distributed generators and electric vehicles in a distribution system using the political optimization algorithm
his paper proposes an effective approach to solve renewable distributed generators (RDGs) and electric vehicle charging station (EVCS) allocation problems in the distribution system (DS) to reduce power loss (PLoss) and enhance voltage profile. The RDGs considered for this work are solar, wind and fuel cell. The uncertainties related to RDGs are modelled using probability distribution functions (PDF). These sources’ best locations and sizes are identified by the voltage stability index (VSI) and political optimization algorithm (POA). Furthermore, EV charging strategies such as the conventional charging method (CCM) and optimized charging method (OCM) are considered to study the method’s efficacy. The developed approach is studied on Indian 28 bus DS. Different cases are considered, such as a single DG, multiple DGs and a combination of DGs and EVs. This placement of multiple DGs along with EVs, considering proper scheduling patterns, minimizes PLoss and considerably improves the voltage profile. Finally, the proposed method is compared with other algorithms, and simulated results show that the POA method produces better results in all aspects
Opinion dynamics in social networks: From models to data
Opinions are an integral part of how we perceive the world and each other.
They shape collective action, playing a role in democratic processes, the
evolution of norms, and cultural change. For decades, researchers in the social
and natural sciences have tried to describe how shifting individual
perspectives and social exchange lead to archetypal states of public opinion
like consensus and polarization. Here we review some of the many contributions
to the field, focusing both on idealized models of opinion dynamics, and
attempts at validating them with observational data and controlled sociological
experiments. By further closing the gap between models and data, these efforts
may help us understand how to face current challenges that require the
agreement of large groups of people in complex scenarios, such as economic
inequality, climate change, and the ongoing fracture of the sociopolitical
landscape.Comment: 22 pages, 3 figure
Moulding student emotions through computational psychology: affective learning technologies and algorithmic governance
Recently psychology has begun to amalgamate with computer science approaches to big data analysis as a new field of ‘computational psychology’ or ‘psycho-informatics,’ as well as with new ‘psycho-policy’ approaches associated with behaviour change science, in ways that propose new ways of measuring, administering and managing individuals and populations. In particular, ‘social-emotional learning’ has become a new focus within education. Supporters of social-emotional learning foresee technical systems being employed to quantify and govern learners’ affective lives, and to modify their behaviours in the direction of ‘positive’ feelings. In this article I identify the core aspirations of computational psychology in education, along with the technical systems it proposes to enact its vision, and argue that a new form of ‘psycho-informatic power’ is emerging as a source of authority and control over education
The Politics of Social Media Manipulation
Disinformation and so-called fake news are contemporary phenomena with rich histories. Disinformation, or the willful introduction of false information for the purposes of causing harm, recalls infamous foreign interference operations in national media systems. Outcries over fake news, or dubious stories with the trappings of news, have coincided with the introduction of new media technologies that disrupt the publication, distribution and consumption of news -- from the so-called rumour-mongering broadsheets centuries ago to the blogosphere recently. Designating a news organization as fake, or der LĂĽgenpresse, has a darker history, associated with authoritarian regimes or populist bombast diminishing the reputation of 'elite media' and the value of inconvenient truths. In a series of empirical studies, using digital methods and data journalism, the authors inquire into the extent to which social media have enabled the penetration of foreign disinformation operations, the widespread publication and spread of dubious content as well as extreme commentators with considerable followings attacking mainstream media as fake
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