The Medical Cyber-physical Systems Activity at EIT: A Look under the Hood

Sonntag, Daniel; Zillner, Sonja; Chakraborty, Samarjit; Lörincz, András; Strömmer, Esko; Serafini, Luciano

doi:10.1109/cbms.2014.83

Cited by 12 publications

(12 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The term 'Cyber-Physical System' was coined by Helen Gill from NSF in 2006 [21]. The federal Networking and Information Technology Research and Development (NITRD) [22] envisions that CPSs can revolutionize agriculture [23]- [26], building control [27]- [30], security [31]- [33], energy management [34], hazard response [35]- [38], healthcare [16], [39], [40], society [41], [42], transportation [43], [44], environment [45] and manufacturing industries [46]- [49]. Even after exploded research interests and investments, the research in CPSs is still in a nascent stage.…”

Section: Modeling Cyber-physical Systemsmentioning

confidence: 99%

Driven by Data or Derived Through Physics? A Review of Hybrid Physics Guided Machine Learning Techniques With Cyber-Physical System (CPS) Focus

2020

View full text Add to dashboard Cite

A multitude of cyber-physical system (CPS) applications, including design, control, diagnosis, prognostics, and a host of other problems, are predicated on the assumption of model availability. There are mainly two approaches to modeling: Physics/Equation based modeling (Model-Based, MB) and Machine Learning (ML). Recently, there is a growing consensus that ML methodologies relying on data need to be coupled with prior scientific knowledge (or physics, MB) for modeling CPS. We refer to the paradigm that combines MB approaches with ML as hybrid learning methods. Hybrid modeling (HB) methods is a growing field within both the ML and scientific communities, and are recognized as an important emerging but nascent area of research. Recently, several works have attempted to merge MB and ML models for the complete exploitation of their combined potential. However, the research literature is scattered and unorganized. So, we make a meticulous and systematic attempt at organizing and standardizing the methods of combining ML and MB models. In addition to that, we outline five metrics for the comprehensive evaluation of hybrid models. Finally, we conclude by shedding some light on the challenges of hybrid models, which we, as a research community, should focus on for harnessing the full potential of hybrid models. An additional feature of this survey is that the hybrid modeling work has been discussed with a focus on modeling cyber-physical systems. INDEX TERMS Cyber-physical systems, deep learning, deep neural networks, hybrid models, model-based, machine learning, physics guided, physics informed, physics prior, theory guided.

show abstract

Section: Modeling Cyber-physical Systemsmentioning

confidence: 99%

Driven by Data or Derived Through Physics? A Review of Hybrid Physics Guided Machine Learning Techniques With Cyber-Physical System (CPS) Focus

2020

View full text Add to dashboard Cite

show abstract

“…The cyber world, in turn, collects these data for processing [1]. Lately, the applications of CPS can be found in different areas of our everyday life-supporting services that include health care and medicine [2,3], process control [4], agriculture [5], smart cars and intelligent transport [6,7], smart grid and smart cities [8,9], environmental monitoring [10] and social networks [11] and many others.…”

Section: Cyber-physical Systemsmentioning

confidence: 99%

Energy management in a cloud‐based cyber‐physical system

Orumwense

Abo-Al-Ez

2021

IET cyber-phys. syst.

View full text Add to dashboard Cite

Cyber-physical systems (CPSs) are embodied systems of highly unified computational, control and communicational elements tightly fused with the physical world. Normally, CPSs are seen to have limited storage and computational abilities due to the fact that they are implemented across several platforms and also embedded into larger systems. The fusion of Cloud computing and CPS gives rise to Cloud-based CPS; Cloud computing will no doubt provide numerous opportunities for CPSs to increase their capabilities by taking advantage of the resources (applications, servers, storage and network capabilities). With this new addition, there will definitely be an increase in energy consumption in the system and this becomes a huge and daunting task that needs to be overcome in actualising this goal. Here, the energy consumption in the network system is being evaluated to ensure an effective data transmission amongst sensor nodes. A simulation environment is considered where a particle swarm optimization algorithm is introduced to optimise and balance the consumption of energy in the system network. To ensure an energyefficient Cloud data control center, an energy consumption model is developed based on resource utilisation. This, however, assists in managing the amount of resources to be utilised for a specific amount of workload.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

show abstract

“…Deterministic models: knowledge-base. In order to realize CPS, we need smart analytical tools to transform experience-based knowledge into evidence-based decision making for sustainable and reliable operation [25]. Components of a CPS come from multiple vendors in diverse engineering disciplines with distinct domain expertise [11,15].…”

Section: 2mentioning

confidence: 99%

“…This gives the possibility for the cyber space to predict future behavior. Another example is in the healthcare domain, where semantic knowledge needs to be integrated with manually and automatically collected low-cost clinical patient data towards clinical decision support [20,25]. Temporal segmentation and/or clustering of episodes can be applied to historical information of patient data to analyze behavior patterns and predict possible future diseases or recovery status.…”

Section: Fio Systemsmentioning

confidence: 99%

“…The design and realization of CPS call for the integration of theoretical models and engineering techniques originating from different disciplines. Examples and areas of application include autonomous driving systems, transportation, smart factories, intelligent manufacturing [12,13], healthcare [25], civil infrastructure [3] (electrical power grid, water resources, communication systems, networked building control), assisted living (consumer appliances, intelligent homes), entertainment.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Cognitive Modeling Approach for Dealing with Challenges in Cyber-Physical Systems

Rill

Lörincz

2019

Studia UBB Informatica

View full text Add to dashboard Cite

In this paper, inspired by our previous works, we propose an architecture for the design and realization of cyber-physical systems (CPS) that considers the spatio-temporal context of events, promotes anomaly detection, facilitates efficient human-computer interaction and is capable of discovering novel human and/or machine knowledge. We view deep neural networks as smart sensors and sensory data from the environment represents the semantic and episodic input to a consistency seeking component of the cyber-space. Starting from a knowledge base infused with a deterministic world assumption, this module can detect anomalies and correct estimation errors by combining the outputs of multiple sensors. We also exploit an episodic description of ongoing situations by integrating temporal segmentation with kernel and low-dimensional embedding based methods. We demonstrate parts of the architecture through illustrative examples on our self-collected driving dataset. Our framework can be related to cognitive science foundations and may facilitate reliable functioning of CPS through integrating traditional AI and deep learning methods with deterministic models and reasoning tools. We expect that such knowledge base and cognition driven approaches of joining deep neural networks will be adopted in complex CPS. This looks like a scalable, and beneficial match between human knowledge and the exploding deep learning technologies.

show abstract

The Medical Cyber-physical Systems Activity at EIT: A Look under the Hood

Cited by 12 publications

References 12 publications

Driven by Data or Derived Through Physics? A Review of Hybrid Physics Guided Machine Learning Techniques With Cyber-Physical System (CPS) Focus

Driven by Data or Derived Through Physics? A Review of Hybrid Physics Guided Machine Learning Techniques With Cyber-Physical System (CPS) Focus

Energy management in a cloud‐based cyber‐physical system

Cognitive Modeling Approach for Dealing with Challenges in Cyber-Physical Systems

Contact Info

Product

Resources

About