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OFFIS-Institute for Information Technology, Germany

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24 Papers

Structured Planning of Hardware and Software Co-simulation Testing of Smart Grids

Topics: Domain-Specific Tools; Hardware in the Loop Simulation; Clean Energy and Power Systems; Co-Simulation; Simulation of Energy Systems

Authors: Rami Elshinawy , Rebeca Acosta , Jan Schwarz and Sebastian Lehnhoff

Affiliation: OFFIS Institute, Escherweg 2, Oldenburg, Germany

Digital Object Identifier: 10.5220/0009820701970208

Proceedings of SIMULTECH 2020, ISBN: 978-989-758-444-2

Quick Abstract

The traditional electricity grid is ought to become a smart grid. One reason is the integration of volatile renewable energy generation, which poses the demand for advanced communication and control technology. This results in a change of the overall system dynamics, which in turn require testing to be done in early stages and with a more holistic approach. However, there is a gap in the transition from system specification and static analysis to an experimental setup validating these specification. This paper demonstrates first steps towards a structured methodology for deriving validation experiments for smart grids from initial system requirements. Our approach aims to fill the introduced gap by integrating Smart Grid Architecture Model (SGAM) for static analysis, Holistic Testing Description (HTD) for experimental analysis, and additional tools in a new workflow. Initial assessment of the workflow has been validated on its ability to select the most suitable components and test beds to perform an experiment. Therefore, two case studies were selected, one in the electricity power domain and another in the electricity market domain.

A Clustering-based Approach to Determine a Standardized Statistic for Daily Activities of Elderly Living Alone

Topics: Home Monitoring; Physiological and Behavioral Modeling

Authors: Alexander Gerka ¹ , Christian Lins ¹ , Max Pfingsthorn ¹ , Marco Eichelberg ¹ , Sebastian Müller ² , Christian Stolle ² and Andreas Hein ²

Affiliations: ¹ OFFIS - Institute for Information Technology, Escherweg 2, Oldenburg and Germany , ² Department for Health Services Research, Carl-von-Ossietzky University, Oldenburg and Germany

Digital Object Identifier: 10.5220/0007369302640271

Proceedings of HEALTHINF 2019, ISBN: 978-989-758-353-7

Quick Abstract

The modeling of behavior by monitoring activities of daily living allows caregivers to recognize early stages of dementia. Therefore, many monitoring systems were presented in recent years. In this work, we present a behavior modeling system that is based only on two adjustable parameters and provides a single standardized output statistic. Therefore, this system enhances the comparison of recent and future activity monitoring systems. The approach is comprised of three parts: First, the clustering of power plug data to detect time windows in which appliances are used regularly. Second, the calculation of a comparison Matrix. Third the test of change using the χ2-statistic. We tested this approach successfully in a seven-month field study with two healthy subjects. We showed that the χ2-statistic reflected how regular activities were performed and that one to two months, depending on the regularity of the performed activities, provide the necessary amount of reference data for our approach to work.

Related Ontology Subjects/Areas/Topics (3)

Applications and Uses

Home Monitoring and Assisted Living Applications

Sensor Networks

Lazy Agents for Large Scale Global Optimization

Topics: Soft Computing; Distributed Problem Solving; Swarm Intelligence; Self Organizing Systems

Authors: Joerg Bremer ¹ and Sebastian Lehnhoff ²

Affiliations: ¹ Department of Computing Science, University of Oldenburg, Uhlhornsweg, Oldenburg and Germany , ² R&D Division Energy, OFFIS – Institute for Information Technology, Escherweg, Oldenburg and Germany

Digital Object Identifier: 10.5220/0007571600720079

Proceedings of ICAART 2019, ISBN: 978-989-758-350-6

Quick Abstract

Optimization problems with rugged, multi-modal Fitness landscapes, non-linear problems, and derivative-free optimization entails challenges to heuristics especially in the high-dimensional case. High-dimensionality also tightens the problem of premature convergence and leads to an exponential increase in search space size. Parallelization for acceleration often involves domain specific knowledge for data domain partition or functional or algorithmic decomposition. We extend a fully decentralized agent-based approach for a global optimization algorithm based on coordinate descent and gossiping that has no specific decomposition needs and can thus be applied to arbitrary optimization problems. Originally, the agent method suffers from likely getting stuck in high-dimensional problems. We extend a laziness mechanism that lets the agents randomly postpone actions of local optimization and achieve a better avoidance of stagnation in local optima. The extension is tested against the original method as well as against established methods. The lazy agent approach turns out to be competitive and often superior in many cases.

Related Ontology Subjects/Areas/Topics (12)

Agents	Distributed Problem Solving	Intelligent Control Systems and Optimization
Artificial Intelligence	Enterprise Information Systems	Self Organizing Systems
Artificial Intelligence and Decision Support Systems	Evolutionary Computing	Soft Computing
Computational Intelligence	Informatics in Control, Automation and Robotics	Swarm Intelligence

A Cascading Chi-shapes based Decoder for Constraint-handling in Distributed Energy Management

Topics: Representation Techniques; Evolutionary Search and Meta-heuristics ; Applications: Games and Entertainment Technologies, Evolutionary Robotics, Evolutionary Art and Design, Industrial and Real World applications, Computational Economics and Finance

Authors: Joerg Bremer ¹ and Sebastian Lehnhoff ²

Digital Object Identifier: 10.5220/0006926101840191

Proceedings of IJCCI 2018, ISBN: 978-989-758-327-8

Quick Abstract

A steadily rising share of small, distributed, and volatile energy units like wind energy converters solar panels, co-generation plants, or similar assigns new tasks and challenges to the smart grid regarding operation and control. The growing complexity of the grid also imposes a growing complexity of constraints that restrict the validity of solutions for operation schedules, resource capacity utilization or grid compliance. Using surrogate models as an abstraction layer has recently become a promising approach for constructing algorithms independently from any knowledge about the actual device or operation restricting constraints. So called decoders as a special constraint handling technique allow for systematically generating feasible solutions directly from a learned surrogate model. Some decoder approaches based on support vector machines have already been implemented, but suffer from performance issues and a sensible parametrization. We propose a new type of decoder based on a cascade of χ-shapes to overcome these problems. The applicability is demonstrated with a simulation study using different types of flexible energy units.

Related Ontology Subjects/Areas/Topics (5)

Artificial Intelligence		Evolutionary Computing		Soft Computing
Computational Intelligence		Representation Techniques

Unsupervised Temporal Segmentation of Skeletal Motion Data using Joint Distance Representation

Topics: Wearable Health Informatics; Development of Assistive Technology; Pattern Recognition and Machine Learning; Medical Informatics

Authors: Christian Lins ¹ , Sebastian M. Müller ² , Max Pfingsthorn ² , Marco Eichelberg ² , Alexander Gerka ² and Andreas Hein ¹

Affiliations: ¹ Carl von Ossietzky University, Germany , ² OFFIS - Institute for Information Technology, Germany

Digital Object Identifier: 10.5220/0006598904780485

Proceedings of HEALTHINF 2018, ISBN: 978-989-758-281-3

Quick Abstract

In this paper, we present an online method for the unsupervised segmentation of skeletal motion capture data for the assessment of unfavorable or harmful postures in the context of musculoskeletal disorders. The long-time motion capture data is segmented into short motion sequences using joint distances of the captured skeleton. We use the difference between joint distance matrices to detect variances in motion dynamics in which the motion is separated into either a dynamic motion or a static posture. Then, the static posture can be evaluated using well-known posture assessment methods such as the Ovako Working postures Analysing System (OWAS) to derive risk factors for musculoskeletal disorders. The algorithm works in real-time so that it can be incorporated in live warning systems for unfavorable or harmful postures. We evaluated the segmentation algorithm by comparing it with results from state-of-the-art offline motion segmentation algorithms as gold standard. Results show that the algorithm approaches the performance of state-of-the-art offline segmentation algorithms.

Related Ontology Subjects/Areas/Topics (9)

Biomedical Engineering	Devices	Pattern Recognition and Machine Learning
Biomedical Signal Processing	Health Information Systems	Physiological Computing Systems
Development of Assistive Technology	Human-Computer Interaction	Wearable Sensors and Systems

Abstract Information Model for Geriatric Patient Treatment - Actors and Relations in Daily Geriatric Care

Topics: Knowledge Management; Evaluation and Use of Healthcare IT; Medical Informatics; Design and Development of Methodologies for Healthcare IT

Authors: Lars Rölker-Denker and Andreas Hein

Affiliation: University of Oldenburg, Germany

Digital Object Identifier: 10.5220/0006106902220229

Proceedings of HEALTHINF 2017/BIOSTEC, ISBN: 978-989-758-213-4

Quick Abstract

The authors propose an abstract information for geriatric care, the geriatric information model (GIM). They adopt an information model from cancer care and introduce characteristics for geriatric care (patient population, multidisciplinary and multi-professional approach, cross-sectoral approach). Actors (patients, physicians, therapists, organisations), information objects, and information relations are defined. The GIM is validated by mapping four typical knowledge processes (multi-professional geriatric team session, interdisciplinary clinical case conferences, tumor boards, transition management) onto the model. The GIM is stated as useful for understanding information flows and relations in geriatric care. All processes for validation can be mapped onto GIM. In future work the GIM should be tested with more knowledge process and could also be used for identifying gaps in the IT support of geriatric care. A study on high and low information quality in geriatric care is also proposed.

Related Ontology Subjects/Areas/Topics (10)

Biomedical Engineering	Health Information Systems	Ontologies and the Semantic Web
Data Engineering	Information Systems Analysis and Specification	Society, e-Business and e-Government
Enterprise Information Systems	Knowledge Management	Web Information Systems and Technologies
Evaluation and Use of Healthcare IT

A Quantum Field Evolution Strategy - An Adaptive Surrogate Approach

Topics: Evolution Strategies; Swarm/Collective Intelligence

Authors: Jörg Bremer ¹ and Sebastian Lehnhoff ²

Affiliations: ¹ University of Oldenburg, Germany , ² OFFIS, Germany

Digital Object Identifier: 10.5220/0006037000210029

Proceedings of ECTA 2016/IJCCI, ISBN: 978-989-758-201-1

Quick Abstract

Evolution strategies have been successfully applied to optimization problems with rugged, multi-modal fitness landscapes, to non linear problems, and to derivative free optimization. Usually evolution is performed by exploiting the structure of the objective function. In this paper, we present an approach that harnesses the adapting quantum potential field determined by the spatial distribution of elitist solutions as guidance for the next generation. The potential field evolves to a smoother surface leveling local optima but keeping the global structure what in turn allows for a faster convergence of the solution set. We demonstrate the applicability and the competitiveness of our approach compared with particle swarm optimization and the well established evolution strategy CMA-ES.

Related Ontology Subjects/Areas/Topics (6)

Artificial Intelligence		Evolution Strategies		Soft Computing
Computational Intelligence		Evolutionary Computing		Swarm/Collective Intelligence

Improving Cascade Classifier Precision by Instance Selection and Outlier Generation

Topics: Machine Learning; Data Mining; Pattern Recognition; Industrial Applications of AI; Data Science

Authors: Judith Neugebauer , Oliver Kramer and Michael Sonnenschein

Affiliation: Carl von Ossietzky University Oldenburg, Germany

Digital Object Identifier: 10.5220/0005702100960104

Proceedings of ICAART 2016, ISBN: 978-989-758-172-4

Quick Abstract

Beside the curse of dimensionality and imbalanced classes, unfavorable data distributions can hamper classification accuracy. This is particularly problematic with increasing dimensionality of the classification task. A classifier that can handle high-dimensional and imbalanced data sets is the cascade classification method for time series. The cascade classifier can compound unfavorable data distributions by projecting the high-dimensional data set onto low-dimensional subsets. A classifier is trained for each of the low-dimensional data subsets and their predictions are aggregated to an overall result. For the cascade classifier, the errors of each classifier accumulate in the overall result and therefore small improvements in each small classifier can improve the classification accuracy. Therefore we propose two methods for data preprocessing to improve the cascade classifier. The first method is instance selection, a technique to select representative examples for the classification task. Furthermore, artificial infeasible examples can improve classification performance. Even if high-dimensional infeasible examples are available, their projection to low-dimensional space is not possible due to projection errors. We propose a second data preprocessing method for generating artificial infeasible examples in low-dimensional space. We show for micro Combined Heat and Power plant power production time series and an artificial and complex data set that the proposed data preprocessing methods increase the performance of the cascade classifier by increasing the selectivity of the learned decision boundaries.

Related Ontology Subjects/Areas/Topics (24)

Artificial Intelligence	Evolutionary Computing	Neurocomputing
Biomedical Engineering	Health Engineering and Technology Applications	Neurotechnology, Electronics and Informatics
Biomedical Signal Processing	Human-Computer Interaction	Pattern Recognition
Computational Intelligence	Industrial Applications of AI	Physiological Computing Systems
Data Manipulation	Knowledge Discovery and Information Retrieval	Sensor Networks
Data Mining	Knowledge-Based Systems	Signal Processing
Databases and Information Systems Integration	Machine Learning	Soft Computing
Enterprise Information Systems	Methodologies and Methods	Symbolic Systems

Activity Recognition Using Non-intrusive Appliance Load Monitoring

Topics: Ambient Intelligence; Ubiquitous Computing Systems and Services; Context-Aware Applications; Pervasive Health; Mobile Computing

Authors: Olaf Wilken , Oliver Kramer , Enno-Edzard Steen and Andreas Hein

Affiliation: University of Oldenburg, Germany

Digital Object Identifier: 10.5220/0004700300400048

Proceedings of PECCS 2014, ISBN: 978-989-758-000-0

Quick Abstract

The recognition of sequences via non-intrusive appliance load monitoring has an important part to play for various applications in healthcare. In our work, we present a system for the detection of daily activities based on the use of appliances. The objective of our activity monitoring system is to maximize the time elder people can stay in their own domestic environment. We propose a system that is able to detect comparably complex activities that may be interrupted by other activities. In the experimental part of our work, a one-month and a half-year field study demonstrate the capabilities of the proposed approach.

Related Ontology Subjects/Areas/Topics (10)

Ambient Intelligence	Mobile Computing	Software Engineering
Context	Paradigm Trends	Telecommunications
Context-Aware Applications	Pervasive Health	Ubiquitous Computing Systems and Services
Mobile and Pervasive Computing

Uncertainty Quantification in Smart Grid Co-simulation Across Heterogeneous Model Domains

Author: Cornelius Steinbrink

Affiliation: OFFIS – Institute for Information Technology, Germany

Proceedings of DCSIMULTECH 2014/SIMULTECH, ISBN: Not Available

Quick Abstract

Smart Grids are complex systems that require systematic testing of the single components and their interaction on various scales. The Smart Energy Simulation and Automation Laboratory (SESA-Lab) is a ﬂexible testing environment that supports modular interaction be- tween hardware and software based simulation. Its core is the real-time dynamic simulator eMEGAsim coupled with the modular Smart Grid simulation framework mosaik. The outlined PhD project aims at im- proving this coupling by increasing the accuracy of the data exchange and setting up a system for uncertainty quantiﬁcation.

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