17,938 research outputs found
ERIGrid Holistic Test Description for Validating Cyber-Physical Energy Systems
Smart energy solutions aim to modify and optimise the operation of existing energy infrastructure. Such cyber-physical technology must be mature before deployment to the actual infrastructure, and competitive solutions will have to be compliant to standards still under development. Achieving this technology readiness and harmonisation requires reproducible experiments and appropriately realistic testing environments. Such testbeds for multi-domain cyber-physical experiments are complex in and of themselves. This work addresses a method for the scoping and design of experiments where both testbed and solution each require detailed expertise. This empirical work first revisited present test description approaches, developed a newdescription method for cyber-physical energy systems testing, and matured it by means of user involvement. The new Holistic Test Description (HTD) method facilitates the conception, deconstruction and reproduction of complex experimental designs in the domains of cyber-physical energy systems. This work develops the background and motivation, offers a guideline and examples to the proposed approach, and summarises experience from three years of its application.This work received funding in the European Communityâs Horizon 2020 Program (H2020/2014â2020)
under project âERIGridâ (Grant Agreement No. 654113)
Layered evaluation of interactive adaptive systems : framework and formative methods
Peer reviewedPostprin
Latent tree models
Latent tree models are graphical models defined on trees, in which only a
subset of variables is observed. They were first discussed by Judea Pearl as
tree-decomposable distributions to generalise star-decomposable distributions
such as the latent class model. Latent tree models, or their submodels, are
widely used in: phylogenetic analysis, network tomography, computer vision,
causal modeling, and data clustering. They also contain other well-known
classes of models like hidden Markov models, Brownian motion tree model, the
Ising model on a tree, and many popular models used in phylogenetics. This
article offers a concise introduction to the theory of latent tree models. We
emphasise the role of tree metrics in the structural description of this model
class, in designing learning algorithms, and in understanding fundamental
limits of what and when can be learned
Controllability of Social Networks and the Strategic Use of Random Information
This work is aimed at studying realistic social control strategies for social
networks based on the introduction of random information into the state of
selected driver agents. Deliberately exposing selected agents to random
information is a technique already experimented in recommender systems or
search engines, and represents one of the few options for influencing the
behavior of a social context that could be accepted as ethical, could be fully
disclosed to members, and does not involve the use of force or of deception.
Our research is based on a model of knowledge diffusion applied to a
time-varying adaptive network, and considers two well-known strategies for
influencing social contexts. One is the selection of few influencers for
manipulating their actions in order to drive the whole network to a certain
behavior; the other, instead, drives the network behavior acting on the state
of a large subset of ordinary, scarcely influencing users. The two approaches
have been studied in terms of network and diffusion effects. The network effect
is analyzed through the changes induced on network average degree and
clustering coefficient, while the diffusion effect is based on two ad-hoc
metrics defined to measure the degree of knowledge diffusion and skill level,
as well as the polarization of agent interests. The results, obtained through
simulations on synthetic networks, show a rich dynamics and strong effects on
the communication structure and on the distribution of knowledge and skills,
supporting our hypothesis that the strategic use of random information could
represent a realistic approach to social network controllability, and that with
both strategies, in principle, the control effect could be remarkable
Explainable AI for engineering design: A unified approach of systems engineering and component-based deep learning
Data-driven models created by machine learning gain in importance in all
fields of design and engineering. They have high potential to assists
decision-makers in creating novel artefacts with better performance and
sustainability. However, limited generalization and the black-box nature of
these models lead to limited explainability and reusability. These drawbacks
provide significant barriers retarding adoption in engineering design. To
overcome this situation, we propose a component-based approach to create
partial component models by machine learning (ML). This component-based
approach aligns deep learning to systems engineering (SE). By means of the
example of energy efficient building design, we first demonstrate better
generalization of the component-based method by analyzing prediction accuracy
outside the training data. Especially for representative designs different in
structure, we observe a much higher accuracy (R2 = 0.94) compared to
conventional monolithic methods (R2 = 0.71). Second, we illustrate
explainability by exemplary demonstrating how sensitivity information from SE
and rules from low-depth decision trees serve engineering. Third, we evaluate
explainability by qualitative and quantitative methods demonstrating the
matching of preliminary knowledge and data-driven derived strategies and show
correctness of activations at component interfaces compared to white-box
simulation results (envelope components: R2 = 0.92..0.99; zones: R2 =
0.78..0.93). The key for component-based explainability is that activations at
interfaces between the components are interpretable engineering quantities. In
this way, the hierarchical component system forms a deep neural network (DNN)
that a priori integrates information for engineering explainability. ...Comment: 17 page
Design and evaluation of a case-based system for modelling exploratory learning behaviour of math generalisation
Exploratory learning environments (ELEs) promote a view of learning that encourages students to construct and/or explore
models and observe the effects of modifying their parameters. The freedom given to learners in this exploration context leads to a
variety of learner approaches for constructing models and makes modelling of learner behaviour a challenging task. To address this
issue, we propose a learner modelling mechanism for monitoring learnersâ actions when constructing/exploring models by modelling
sequences of actions reflecting different strategies in solving a task. This is based on a modified version of case-based reasoning for
problems with multiple solutions. In our formulation, approaches to explore the task are represented as sequences of simple cases
linked by temporal and dependency relations, which are mapped to the learnersâ behaviour in the system by means of appropriate
similarity metrics. This paper presents the development and validation of the modelling mechanism. The model was validated in the
context of an ELE for mathematical generalisation using data from classroom sessions and pedagogically-driven learning scenarios
The role of pedagogical tools in active learning: a case for sense-making
Evidence from the research literature indicates that both audience response
systems (ARS) and guided inquiry worksheets (GIW) can lead to greater student
engagement, learning, and equity in the STEM classroom. We compare the use of
these two tools in large enrollment STEM courses delivered in different
contexts, one in biology and one in engineering. The instructors studied
utilized each of the active learning tools differently. In the biology course,
ARS questions were used mainly to check in with students and assess if they
were correctly interpreting and understanding worksheet questions. The
engineering course presented ARS questions that afforded students the
opportunity to apply learned concepts to new scenarios towards improving
students conceptual understanding. In the biology course, the GIWs were
primarily used in stand-alone activities, and most of the information necessary
for students to answer the questions was contained within the worksheet in a
context that aligned with a disciplinary model. In the engineering course, the
instructor intended for students to reference their lecture notes and rely on
their conceptual knowledge of fundamental principles from the previous ARS
class session in order to successfully answer the GIW questions. However, while
their specific implementation structures and practices differed, both
instructors used these tools to build towards the same basic disciplinary
thinking and sense-making processes of conceptual reasoning, quantitative
reasoning, and metacognitive thinking.Comment: 20 pages, 5 figure
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