InformaApp - Publikationen

Energy system models help to find the optimal technology mixes for decarbonization strategies in countries worldwide. To reduce the modeling effort and analyze as many countries as possible, this paper proposes a novel approach of energy system archetypes which can be directly evaluated. These archetypes classify similar countries worldwide independently from their geographic location. Advantages of this idea are the setup of a transferable global database allowing for data reconstruction between countries, market size estimations, and the ability to compare peer countries facing similar challenges. To enable such modeling, a framework is developed in which the archetypes are defined, standardized modeling rules are developed, and the results are evaluated for validation. In a benchmark against simple geographic classifications, the presented clustering approach, which results in 15 archetypes, improves the variance between all countries and their corresponding archetypes by 44% compared to the variance between the countries and their geographic sub-regions. The model results of these archetypes state the need of balancing technologies for the daily cycle of photovoltaic generation and the general importance of flexibility in future decarbonized energy systems. Overall, the results confirm that archetypes are an adequate approach to derive the set of solutions for the decarbonization of worldwide countries.

Beitrag (Sammelband oder Tagungsband)

Sebastian Wilhelm

Exploiting Home Infrastructure Data for the good: Emergency Detection by reusing existing Data Sources

[Accepted for publication]

Advances in Intelligent Systems and Computing

2021

Monitoring people within their residence can enable elderly to live a self-determined life in their own home environment for a longer period of time. Therefore commonly activity profiles of the residents are created using various sensors in the house. Deviations from the typical activity profile may indicate an emergency situation. An alternative approach for monitoring people within their residence we investigates within our research is reusing existing data sources instead of installing additional sensors. In private households there are already numerous data sources such as smart meters weather station routers or voice assistants available. Intelligent algorithms can be used to evaluate this data and conclude on personal activities. This in turn allows the creation of activity profiles of the residents without using external sensor technology.This work outlines the research gap in reusing existing data sources for Human Activity Recognition (HAR) and emergency detection which we intend to fill with our further work.

Beitrag (Sammelband oder Tagungsband)

Dietmar Jakob

Acceptance and Practically of Voice Assistance Systems in the everyday life of seniors: A study design

[Accepted for publication]

Advances in Intelligent Systems and Computing

2021

Voice assistance systems (VAs) are becoming more popular. For Digital Natives these systems are almost part of everyday life. Does this technology also have the potential to facilitate access to digital services for persons aged 55+? Using the example of Amazon's "Echo" devices our research intends to provide a survey of the extent to which VAs are known to the target group how many elderly people own these systems and whether there are any reservations. In addition test persons will be investigated which form of interaction seems easiest for the solution of the tasks and how the operation of VAs differs from the operation of mobile devices to be learned and applied. The evaluation will include statements on socio-demographic and ethnographic aspects. Various Amazon Echo devices are installed in 20 senior households (and additionally in their family households) in order to test user acceptance and its benefits under real-world conditions.

Beitrag (Sammelband oder Tagungsband)

Sebastian Wilhelm

Activity-Monitoring in Private Households for Emergency Detection: A Survey of Common Methods and Existing Disaggregable Data Sources

[Accepted for publication]

Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies

2021

Ambient-Assisted Living (AAL) technologies can enable the elderly people to live a self-determined life in their own home environment instead of hospitals and retirement homes for a longer period of time. Hence AAL systems are not only used for everyday support but also for the detection of potential emergency situ- ations and for triggering notification chains. For this purpose the people are usually continuously monitored within their residents by ambient or wearable sensors to detect deviations in their daily behavior.This work surveys common used technologies for Human Activity Recognition (HAR) / Human Presence De- tection (HPD) which is the basis for emergency detection. Furthermore by examining various home automa- tion software existing data sources from the residential infrastructure are identified that would be suitable for detecting personal activities.

Sebastian Wilhelm, Dietmar Jakob, Jakob Kasbauer, Diane Ahrens

Beitrag (Sammelband oder Tagungsband)

GeLaP: German Labeled Dataset for Power Consumption

[Accepted for publication]

Proceedings of the 6th International Congress on Information and Communication Technology

2021

Digital F: Angewandte Informatik S: TC Freyung

Due to the increasing spread of smart meters numerous researchers are currently working on disaggregating the power consumption data. This procedure is commonly known as Non-Intrusive Load Monitoring (NILM). However most approaches to energy disaggregation first require a labeled dataset to train these algorithms.In this paper we present a new labeled power consumption dataset that was collected in 20 private households in Germany between September 2019 and July 2020. For this purpose the total power consumption of each household was measured with a commercial available smart meter and the individual consumption data of 10 selected household appliances were collected.

Zeitschriftenartikel

J. Trager, L. Kalová, Raphaela Pagany, Wolfgang Dorner

Warning apps for road safety – a technological and economical perspective for autonomous driving

[Article submitted]

International Journal of Human–Computer Interaction

2021

DOI: 10.1109/CBI49978.2020.00033

Beitrag (Sammelband oder Tagungsband)

Q. Yang, Mouzhi Ge, M. Helfert

Developing Reliable Taxonomic Features for Data Warehouse Architectures

2020 IEEE 22nd Conference on Business Informatics (CBI)

2020

ISBN: 978-1-7281-9926-9

Since there is a large variety of data warehouse architectures with different structures and components, it is very difficult and time-consuming to systematically analyse them and obtain insights from those architectures. One effective way to understand those architectures is using a taxonomy to classify them. However, most of the taxonomic features are derived in an ad-hoc way and the reliability of those features is unknown. This paper therefore is to develop a set of reliable features by modeling different data warehouse architectures and further generate the structural knowledge represented by a taxonomy. This taxonomy is further validated by evaluating two real-world data warehouse architectures from IBM and Facebook.

DOI: 10.1145/3386392.3397590

Beitrag (Sammelband oder Tagungsband)

M. Elahi, N. El Ioini, A. Alexander Lambrix, Mouzhi Ge

Exploring Personalized University Ranking and Recommendation

UMAP '20: 28th ACM Conference on User Modeling, Adaptation and Personalization

2020

Finding the right university to study is still a challenge for many people due to the large number of universities worldwide. Although there exist a number of global university rankings, they provide non# personalized rankings as one-size-fits-all solution. This becomes an issue since different people may have different preferences and considerations in mind, when choosing the university to study. This paper addresses this problem and presents a Recommender System to generate a personalized ranking list based on users particular preferences. The system is capable of eliciting users preferences, provided as ratings for universities, building predictive models on the preference data, and generating a personalized university ranking list that is tailored to the particular preferences and needs of the users. We performed two sets of experiments. First, we conducted an offline experiment using a dataset of user preferences, collected by the early version of our system. This allowed us to cross-validate and compare different recommender algorithms and choose the most accurate recommender algorithm that can better suit the particular problem at hand. We integrated the chosen algorithm in the final implementation of our system. As the follow-up, we performed a user study in order to analyze whether or not the final version of our system is usable from the perception of users. The results showed that the system has scored well above the benchmark and users assessed it as "good" in term of usability.

DOI: 10.1109/ICSC.2020.00050

Beitrag (Sammelband oder Tagungsband)

F. Persia, D. D'Auria, Mouzhi Ge

Improving Learning System Performance with Multimedia Semantics

2020 IEEE 14th International Conference on Semantic Computing (ICSC)

2020

Nowadays, different new learning methodologies have been proposed to achieve effective learning in University education. One of the most promising methodologies for teaching computer science is multimedia-based education. In order to empower the performance within the online learning platforms, such as Moodle or OLE, this paper proposes to integrate the multimedia-based education to learning systems, and conducts an experiment with the operating system course. We show that exploiting multimedia, such as educational video and smart text, can significantly improve the student's learning performance in terms of exam grade and knowledge transfer. Further, the paper presents a real-world case study depicting how to enhance the performance of learning platform with multimedia semantics.

Beitrag (Sammelband oder Tagungsband)

H. Bangui, Mouzhi Ge, B. Buhnova

Improving Big Data Clustering for Jamming Detection in Smart Mobility

ICT Systems Security and Privacy Protection, Cham, Switzerland, vol. 580

2020

ISBN: 978-3-030-58200-5

Smart mobility, with its urban transportation services ranging from real-time traffic control to cooperative vehicle infrastructure systems, is becoming increasingly critical in smart cities. These smart mobility services thus need to be very well protected against a variety of security threats, such as intrusion, jamming, and Sybil attacks. One of the frequently cited attacks in smart mobility is the jamming attack. In order to detect the jamming attacks, different anti-jamming applications have been developed to reduce the impact of malicious jamming attacks. One important step in anti-jamming detection is to cluster the vehicular data. However, it is usually very time-consuming to detect the jamming attacks that may affect the safety of roads and vehicle communication in real-time. Therefore, this paper proposes an efficient big data clustering model, coresets-based clustering, to support the real-time detection of jamming attacks. We validate the model efficiency and applicability in the context of a typical smart mobility system: Vehicular Ad-hoc Network, known as VANET.