30,151 research outputs found
Developing a distributed electronic health-record store for India
The DIGHT project is addressing the problem of building a scalable and highly available information store for the Electronic Health Records (EHRs) of the over one billion citizens of India
Evaluation of Cognitive Architectures for Cyber-Physical Production Systems
Cyber-physical production systems (CPPS) integrate physical and computational
resources due to increasingly available sensors and processing power. This
enables the usage of data, to create additional benefit, such as condition
monitoring or optimization. These capabilities can lead to cognition, such that
the system is able to adapt independently to changing circumstances by learning
from additional sensors information. Developing a reference architecture for
the design of CPPS and standardization of machines and software interfaces is
crucial to enable compatibility of data usage between different machine models
and vendors. This paper analysis existing reference architecture regarding
their cognitive abilities, based on requirements that are derived from three
different use cases. The results from the evaluation of the reference
architectures, which include two instances that stem from the field of
cognitive science, reveal a gap in the applicability of the architectures
regarding the generalizability and the level of abstraction. While reference
architectures from the field of automation are suitable to address use case
specific requirements, and do not address the general requirements, especially
w.r.t. adaptability, the examples from the field of cognitive science are well
usable to reach a high level of adaption and cognition. It is desirable to
merge advantages of both classes of architectures to address challenges in the
field of CPPS in Industrie 4.0
Conceptual graph-based knowledge representation for supporting reasoning in African traditional medicine
Although African patients use both conventional or modern and traditional healthcare simultaneously, it has been proven that 80% of people rely on African traditional medicine (ATM). ATM includes medical activities stemming from practices, customs and traditions which were integral to the distinctive African cultures. It is based mainly on the oral transfer of knowledge, with the risk of losing critical knowledge. Moreover, practices differ according to the regions and the availability of medicinal plants. Therefore, it is necessary to compile tacit, disseminated and complex knowledge from various Tradi-Practitioners (TP) in order to determine interesting patterns for treating a given disease. Knowledge engineering methods for traditional medicine are useful to model suitably complex information needs, formalize knowledge of domain experts and highlight the effective practices for their integration to conventional medicine. The work described in this paper presents an approach which addresses two issues. First it aims at proposing a formal representation model of ATM knowledge and practices to facilitate their sharing and reusing. Then, it aims at providing a visual reasoning mechanism for selecting best available procedures and medicinal plants to treat diseases. The approach is based on the use of the Delphi method for capturing knowledge from various experts which necessitate reaching a consensus. Conceptual graph formalism is used to model ATM knowledge with visual reasoning capabilities and processes. The nested conceptual graphs are used to visually express the semantic meaning of Computational Tree Logic (CTL) constructs that are useful for formal specification of temporal properties of ATM domain knowledge. Our approach presents the advantage of mitigating knowledge loss with conceptual development assistance to improve the quality of ATM care (medical diagnosis and therapeutics), but also patient safety (drug monitoring)
Compositional Falsification of Cyber-Physical Systems with Machine Learning Components
Cyber-physical systems (CPS), such as automotive systems, are starting to
include sophisticated machine learning (ML) components. Their correctness,
therefore, depends on properties of the inner ML modules. While learning
algorithms aim to generalize from examples, they are only as good as the
examples provided, and recent efforts have shown that they can produce
inconsistent output under small adversarial perturbations. This raises the
question: can the output from learning components can lead to a failure of the
entire CPS? In this work, we address this question by formulating it as a
problem of falsifying signal temporal logic (STL) specifications for CPS with
ML components. We propose a compositional falsification framework where a
temporal logic falsifier and a machine learning analyzer cooperate with the aim
of finding falsifying executions of the considered model. The efficacy of the
proposed technique is shown on an automatic emergency braking system model with
a perception component based on deep neural networks
Requirement verification in simulation-based automation testing
The emergence of the Industrial Internet results in an increasing number of
complicated temporal interdependencies between automation systems and the
processes to be controlled. There is a need for verification methods that scale
better than formal verification methods and which are more exact than testing.
Simulation-based runtime verification is proposed as such a method, and an
application of Metric temporal logic is presented as a contribution. The
practical scalability of the proposed approach is validated against a
production process designed by an industrial partner, resulting in the
discovery of requirement violations.Comment: 4 pages, 2 figures. Added IEEE copyright notic
Sciduction: Combining Induction, Deduction, and Structure for Verification and Synthesis
Even with impressive advances in automated formal methods, certain problems
in system verification and synthesis remain challenging. Examples include the
verification of quantitative properties of software involving constraints on
timing and energy consumption, and the automatic synthesis of systems from
specifications. The major challenges include environment modeling,
incompleteness in specifications, and the complexity of underlying decision
problems.
This position paper proposes sciduction, an approach to tackle these
challenges by integrating inductive inference, deductive reasoning, and
structure hypotheses. Deductive reasoning, which leads from general rules or
concepts to conclusions about specific problem instances, includes techniques
such as logical inference and constraint solving. Inductive inference, which
generalizes from specific instances to yield a concept, includes algorithmic
learning from examples. Structure hypotheses are used to define the class of
artifacts, such as invariants or program fragments, generated during
verification or synthesis. Sciduction constrains inductive and deductive
reasoning using structure hypotheses, and actively combines inductive and
deductive reasoning: for instance, deductive techniques generate examples for
learning, and inductive reasoning is used to guide the deductive engines.
We illustrate this approach with three applications: (i) timing analysis of
software; (ii) synthesis of loop-free programs, and (iii) controller synthesis
for hybrid systems. Some future applications are also discussed
- âŠ