148 research outputs found
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
3D Surface Reconstruction of Plant Seeds by Volume Carving: Performance and Accuracies
We describe a method for 3D reconstruction of plant seed surfaces, focusing on small seeds with diameters as small as 200 μm. The method considers robotized systems allowing single seed handling in order to rotate a single seed in front of a camera. Even though such systems feature high position repeatability, at sub-millimeter object scales, camera pose variations have to be compensated. We do this by robustly estimating the tool center point from each acquired image. 3D reconstruction can then be performed by a simple shape-from-silhouette approach. In experiments we investigate runtimes, theoretically achievable accuracy, experimentally achieved accuracy, and show as a proof of principle that the proposed method is well sufficient for 3D seed phenotyping purposes
Cognitive Capabilities for the CAAI in Cyber-Physical Production Systems
This paper presents the cognitive module of the cognitive architecture for
artificial intelligence (CAAI) in cyber-physical production systems (CPPS). The
goal of this architecture is to reduce the implementation effort of artificial
intelligence (AI) algorithms in CPPS. Declarative user goals and the provided
algorithm-knowledge base allow the dynamic pipeline orchestration and
configuration. A big data platform (BDP) instantiates the pipelines and
monitors the CPPS performance for further evaluation through the cognitive
module. Thus, the cognitive module is able to select feasible and robust
configurations for process pipelines in varying use cases. Furthermore, it
automatically adapts the models and algorithms based on model quality and
resource consumption. The cognitive module also instantiates additional
pipelines to test algorithms from different classes. CAAI relies on
well-defined interfaces to enable the integration of additional modules and
reduce implementation effort. Finally, an implementation based on Docker,
Kubernetes, and Kafka for the virtualization and orchestration of the
individual modules and as messaging-technology for module communication is used
to evaluate a real-world use case
What Makes Agile Test Artifacts Useful? An Activity-Based Quality Model from a Practitioners' Perspective
Background: The artifacts used in Agile software testing and the reasons why
these artifacts are used are fairly well-understood. However, empirical
research on how Agile test artifacts are eventually designed in practice and
which quality factors make them useful for software testing remains sparse.
Aims: Our objective is two-fold. First, we identify current challenges in using
test artifacts to understand why certain quality factors are considered good or
bad. Second, we build an Activity-Based Artifact Quality Model that describes
what Agile test artifacts should look like. Method: We conduct an industrial
survey with 18 practitioners from 12 companies operating in seven different
domains. Results: Our analysis reveals nine challenges and 16 factors
describing the quality of six test artifacts from the perspective of Agile
testers. Interestingly, we observed mostly challenges regarding language and
traceability, which are well-known to occur in non-Agile projects. Conclusions:
Although Agile software testing is becoming the norm, we still have little
confidence about general do's and don'ts going beyond conventional wisdom. This
study is the first to distill a list of quality factors deemed important to
what can be considered as useful test artifacts
Explanation Needs in App Reviews: Taxonomy and Automated Detection
Explainability, i.e. the ability of a system to explain its behavior to
users, has become an important quality of software-intensive systems. Recent
work has focused on methods for generating explanations for various algorithmic
paradigms (e.g., machine learning, self-adaptive systems). There is relatively
little work on what situations and types of behavior should be explained. There
is also a lack of support for eliciting explainability requirements. In this
work, we explore the need for explanation expressed by users in app reviews. We
manually coded a set of 1,730 app reviews from 8 apps and derived a taxonomy of
Explanation Needs. We also explore several approaches to automatically identify
Explanation Needs in app reviews. Our best classifier identifies Explanation
Needs in 486 unseen reviews of 4 different apps with a weighted F-score of 86%.
Our work contributes to a better understanding of users' Explanation Needs.
Automated tools can help engineers focus on these needs and ultimately elicit
valid Explanation Needs
Simulation and Optimization of Cyclone Dust Separators
Cyclone Dust Separators are devices often used to filter solid particles from flue gas. Such cyclones are supposed to filter as much solid particles from the carrying gas as possible. At the same time, they should only introduce a minimal pressure loss to the system. Hence, collection efficiency has to be maximized and pressure loss minimized. Both the collection efficiency and pressure loss are heavily influenced by the cyclones geometry. In this paper, we optimize seven geometrical parameters of an analytical cyclone model. Furthermore, noise variables are introduced to the model, representing the non-deterministic structure of the real-world problem. This is used to investigate robustness and sensitivity of solutions. Both the deterministic as well as the stochastic model are optimized with an SMS-EMOA. The SMS-EMOA is compared to a single objective optimization algorithm. For the harder, stochastic optimization problem, a surrogate-model-supported SMS-EMOA is compared against the model-free SMS-EMOA. The model supported approach yields better solutions with the same run-time budget
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