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35th International Conference on Principles of Diagnosis and Resilient systems, DX 2024

作者： Mukhtar, Adil Hirsch, Thomas Schweiger, Gerald Institute of Software Technology Graz University of Technology Austria Vienna University of Technology Austria

ISBN: (纸本)9783959773560

Real-world automated systems such as building automation, power plants, and more have benefited from data-driven learning methodologies for anomaly detection and diagnosis. Typically, these methodologies heavily rely on prior knowledge related to abnormal operations, i.e., data points labeled as anomalies. However, in practice, such labelled data points are often unavailable which poses challenges in effective anomaly detection, particularly in diagnosis. In this paper, we propose One-class Classification Cluster ENsembles (OCCEN) anomaly detection and diagnosis approach for multivariate time series data. OCCEN utilizes one-class classification learning methods for anomaly detection followed by the decomposition of anomalies into multiple clusters. Then each cluster is treated as a binary classification problem and classifiers are trained to learn cluster representations. These trained models in combination with explainable AI models are used to generate a ranked list of diagnoses, i.e., features. Finally, we re-rank those features to account for temporal dependencies through the dynamic time-warping technique. The practical evaluation of OCCEN for air handling units (AHU) demonstrates its effectiveness in identifying faults. The framework consistently outperforms the baseline in fault diagnosis, as higher scores are observed for detection and diagnostic evaluation metrics, including F1 score, intersection over union, HitRate@k, and RootCause@k. © Adil Mukhtar, Thomas Hirsch, and Gerald Schweiger.

关键词： Time series

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Immersive virtual reality for extending the potential of building information modeling in architecture,engineering,and construction sector:systematic review

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International Journal of Digital Earth 2022年第1期15卷 503-526页

作者： Saeed Safikhani Stephan Keller Gerald Schweiger Johanna Pirker Institute of Interactive Systems and Data Science Graz University of TechnologyGrazAustria Institute of Software Technology Graz University of TechnologyGrazAustria

The field of architecture,engineering,and construction(AEC)is continually striving to use resources efficiently and manage complex *** more than ever,there is a strong need for digital transformation in *** improvement in the accessibility of consumer-based head-mounted displays(HMD)is attracting different entertainment and research fields to immersive virtual reality(VR)*** Information Modeling(BIM)is known as a promising technology in *** full potential of BIM is not yet employed to empower this field,however,and this could be a result of some barriers still to be surmounted by BIM in both technological and management *** of these barriers is the communication and collaboration between design,construction,operation,and maintenance *** can fill this gap by providing additional capabilities for BIM which either were not available before or were not possible to employ in practical *** this paper,we systematically review recent research around the application of VR in BIM and discuss the results using the PRISMA *** discuss the most commonly used technologies,software,and evaluation methods and the various applications of VR in the reviewed ***,we extend the discussion by summarizing the potential future work in this area.

关键词： Building information modeling architecture engineering construction virtual reality BIM VR

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Hierarchical Learning of Generative Automaton Models from Sequential Data 22nd

Hierarchical Learning of Generative Automaton Models from ...

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22nd International Conference on software Engineering and Formal Methods, SEFM 2024

作者： von Berg, Benjamin Aichernig, Bernhard K. Rindler, Maximilian Štern, Darko Tappler, Martin Institute of Software Technology Graz University of Technology Graz Austria TU Wien Vienna Austria AVL List GmbH Graz Austria

ISBN: (纸本)9783031773815

Passive automata learning is a method for inferring automaton models from a given collection of observations of system behavior (traces). It has been applied to reactive systems with probabilistic behavior. In particular, IOAlergia is a well known algorithm for inferring models in the form of deterministically labeled Markov decision processes from system traces. The quality of the resulting model depends heavily on the provided data set and suffers if data is scarce. However, in many cases additional knowledge about the system is available. This work aims to incorporate knowledge about system modes into the learning process in order to improve model quality for low-data scenarios. This is done by splitting the traces according to system modes, learning individual models for each mode and combining those models into one model with sub-regions corresponding to individual system modes. In our evaluation on artificial models, our method outperforms the baseline in at least 90 % of cases for all considered metrics. This method was developed to learn generative models of human driving behavior. Data from recorded test drives on highways was used to learn a hierarchical stochastic model of typical acceleration behavior of human drivers. In the automotive industry, such models make the simulations of driving emissions more realistic. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Markov processes

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ML-based Fault Tree Analysis of Industrial Router Reliability in the System IIoT

ML-based Fault Tree Analysis of Industrial Router Reliabilit...

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2024 International Conference on Applied Mathematics and Computer Science, ICAMCS 2024

作者： Kolisnyk, Maryna Jantsch, Axel Networks and cybersecurity of National aerospace university 'KhAI' Department of computer systems Kharkiv Ukraine Institute of computer technologies of Vienna University of Technology 'TU Wien' Vienna Austria

ISBN: (纸本)9798350363807

For today's complex and expensive systems, such as an industrial router in an Industrial Internet of Things (IIoT) network, it is important to ensure high operational reliability. The router is a complex system consisting of many subsystems, and an important task is to identify possible failures and faults of its subsystems in order to prevent them in time, or to parry and eliminate them. One of the methods of system reliability assessment is the method of cutting, which includes fault tree analysis (FTA). FTA allows, using the obtained and analysed statistical data, to build a fault tree with a given level of detail, and to determine the probabilities of failures of one or another subsystem. In this paper a detailed tree of failures and failures of hardware (HW), software (SW), and hardware-software (HW-SW) of the industrial router of the IIoT system is developed. The tree is built with the use of program code, using ensemble supervised ML algorithms in Python. The obtained graphical dependencies and obtained probabilities of failures of router's subsystems as a part of quantitative analysis, allowed us to determine for the given initial data the most exposed to the risk of failure of the subsystem of the industrial router in the system IIoT. © 2024 IEEE.

关键词： Fault tree analysis

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Improved Detection and Interpretation of Multilingual Signboards in Natural Scene for Visually Impaired People

Improved Detection and Interpretation of Multilingual Signbo...

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2023 IEEE International Conference on Data and software Engineering, ICoDSE 2023

作者： Zaman, Qamar Khusro, Shah Tjoa, A Min University of Peshawar Department of Computer Science Peshawar25120 Pakistan Institute of Information Systems Engineering Vienna University of Technology Vienna1040 Austria

ISBN: (纸本)9798350381382

Visually impaired individuals face numerous challenges in localization, navigation, reading content, and communicating with others. Unlike sighted individuals who rely on vision in the physical environment to reach their destinations, visually impaired individuals require assistive technologies and smartphone-based applications that employ computer vision to overcome the issues of obstacle detection, read signboards, and navigate in natural scenes. Signboards with text information serve as crucial indicators for navigation and recognition in daily life. However, detecting and interpreting signboard text in natural scenes poses difficulties for visually impaired individuals. Researchers have made efforts to address issues such as text detection on signboards, reducing blurriness, removing backgrounds, improving lighting conditions, and enhancing accuracy and response time of recognition. The primary challenges lie in the detection and interpretation of multilingual and diverse content on signboards in real-time. The proposed system presents a methodology for real-time detection and interpretation of diverse content on signboards, including text in English and Urdu along with phone numbers, for visually impaired individuals. An Android-based application is developed to improve text detection and recognition methods for multilingual texts, providing vital navigation information to visually impaired individuals in real-time. The methodology and application are evaluated, and the results demonstrate promising outcomes. copy;2023 IEEE. © 2023 IEEE.

关键词： Navigation

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Method of PLC’s Dependability (Reliability and Cybersecurity) Ensuring in IIoT systems

Method of PLC’s Dependability (Reliability and Cybersecurit...

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Future of Information and Communication Conference, FICC 2025

作者： Kolisnyk, Maryna Jantsch, Axel Department of Computer Systems Networks and Cybersecurity National Aerospace University "KhAI" Kharkiv Ukraine Institute of Computer Technology Vienna University of Technology "TU Wien" Vienna Austria

ISBN: (纸本)9783031853623

Industrial Internet of Things (IIoT) systems are actively and successfully implemented at enterprises. The variety of data transfer technologies and architectures used in IIoT, the large number of unreliable devices connected to a single Internet network, create risks of anomalies in the functioning of IIoT subsystems and the system as a whole due to their failures and successful cyber-attacks on them. One of the important hardware (HW) and software (SW) subsystems of IIoT is a large modular PLC, which can contain vulnerabilities in both SW and HW. This paper proposes a new method for ensuring the dependability (as reliability and cybersecurity) of a PLC with using of the large modular PLC architecture system analysis, an analysis of possible vulnerabilities of the PLC subsystem, the research of the PLC’s availability indices based on the mathematical apparatus of Markov processes, and the development of appropriate recommendations. This research is interdisciplinary and allows us to assess and ensure both cybersecurity and reliability of PLCs, and will enable manufacturers and operators to make correct and timely decisions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Industrial internet of things (IIoT)

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A Data-driven Analysis of a Cloud Data Center: Statistical Characterization of Workload, Energy and Temperature 23

A Data-driven Analysis of a Cloud Data Center: Statistical C...

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Proceedings of the IEEE/ACM 16th International Conference on Utility and Cloud Computing

作者： Shashikant Ilager Adel N Toosi Mayank Raj Jha Ivona Brandic Rajkumar Buyya Institute of Information Systems Engineering Vienna University of Technology Vienna Austria Department of Software Systems and Cybersecurity Faculty of Information Technology Monash University Melbourne Victoria Australia Università degli Studi dell'Aquila L'Aquila Itlay Vienna University of Technology Vienna Austria The University of Melbourne Melbourne Australia

ISBN: (纸本)9798400702341

To efficiently manage large-scale cloud data centers, it is critical to understand data centers' workload, energy, and thermal characteristics and their impact on the system through data-driven analysis. However, most publicly available traces solely focuses on application workloads ignoring energy and thermal aspects, forcing existing studies to rely on inaccurate and unrealistic analytical or simulation models. In this paper, we present a comprehensive data-driven analysis of a production cloud data center. We monitor and collect the physical machine-level metrics such as resource utilization, energy, and temperature for up to nine months, with a system size of over 26000 CPU cores hosting, on average 1300 virtual machines. We perform a systematic statistical analysis to characterize the monitored data, and study their distributions, variations, trends, and inter-dependencies. We also develop data-driven models to predict resource usage and energy consumption of a physical machine, and demonstrate the usefulness of our dataset through this use case. Our study reveals interesting insights into the energy and thermal phenomena of a data center. The outcome of this analysis helps to increase infrastructure efficiency and long-term strategic planning and improve key business KPIs. The ope-sourced dataset and artefacts enables to investigate new optimization approaches and use cases by researchers.

关键词： data centers workload characterization energy and thermal analysis workload and energy forecasting

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Automatic Root Length Estimation from Images Acquired In Situ without Segmentation

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Plant Phenomics 2024年第1期6卷 79-93页

作者： Faina Khoroshevsky Kaining Zhou Sharon Chemweno Yael Edan Aharon Bar-Hillel Ofer Hadar Boris Rewald Pavel Baykalov Jhonathan EEphrath Naftali Lazarovitch Department of Industrial Engineering and Management Ben-Gurion University of the NegevBeer ShevaIsrael The Jacob Blaustein Center for Scientific Cooperation The Jacob Blaustein Institutes for Desert ResearchBen-Gurion University of the NegevSede BoqerIsrael The Albert Katz International School for Desert Studies The Jacob Blaustein Institutes for Desert ResearchBen-Gurion University of the NegevSede BoqerIsrael Department of Communication Systems Engineering School of Electrical and Computer EngineeringBen-Gurion University of the NegevBeer ShevaIsrael Institute of Forest Ecology Department of Forest and Soil SciencesUniversity of Natural Resources and Life SciencesVienna(BOKU)ViennaAustria Faculty of Forestry and Wood Technology Mendel University in BrnoBrnoCzech Republic Vienna Scientific Instruments GmbH AllandAustria French Associates Institute for Agriculture and Biotechnology of Drylands The Jacob Blaustein Institutes for Desert ResearchBen-Gurion University of the NegevSede BoqerIsrael

Image-based root phenotyping technologies,including the minirhizotron(MR),have expanded our understanding of the in situ root responses to changing environmental *** conventional manual methods used to analyze MR images are time-consuming,limiting their *** study presents an adaptation of our previously developed convolutional neural network-based models to estimate the total(cumulative)root length(TRL)per MR image without requiring *** data were derived from manual annotations in Rootfly,commonly used software for MR image *** compared TRL estimation with 2 models,a regression-based model and a detection-based model that detects the annotated points along the ***,the detection-based model can assist in examining human annotations by providing a visual inspection of roots in MR *** models were trained and tested with 4,015 images acquired using 2 MR system types(manual and automated)and from 4 crop species(corn,pepper,melon,and tomato)grown under various abiotic *** datasets are made publicly available as part of this *** coefficients of determination(R2),between the measurements made using Rootfly and the suggested TRL estimation models were 0.929 to 0.986 for the main datasets,demonstrating that this tool is accurate and *** analyses were conducted to examine the effects of(a)the data acquisition system and thus the image quality on the models'performance,(b)automated differentiation between images with and without roots,and(c)the use of the transfer learning *** approaches can support precision agriculture by providing real-time root growth information.

关键词： Image Root image

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DiLaAg - digitalisation and innovation laboratory in agricultural sciences 10

DiLaAg - digitalisation and innovation laboratory in agricul...

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10th European Conference on Precision Livestock Farming, ECPLF 2022

作者： Pichlbauer, B. Chapa-Gonzalez, J.-M. Purcell, W. Mallinger, K. Koppensteiner, L. Kitzler, F. Pejakovic, V. Medel-Jimenez, F.J. Krexner, T. Iwersen, M. Neubauer, T. Neugschwandtner, R. Bauer, A. Kral, I. Motsch, V. Korth, F. Drillich, M. Rauber, A. Tjoa, A.M. Kaul, H.-P. Gronauer, A. Clinical Unit for Herd Health Management in Ruminants University Clinic for Ruminants Department for Farm Animals and Veterinary Public Health University of Veterinary Medicine Vienna Austria Information and Software Engineering Group Institute of Information Systems Engineering University of Technology Vienna Austria Institute of Agronomy Department of Crop Sciences University of Natural Resources and Life Sciences Vienna Austria Institute of Agricultural Engineering Department of Sustainable Agricultural Systems University of Natural Resources and Life Sciences Vienna Austria

ISBN: (纸本)9788396536006

Sustainable production and development in livestock sectors are critical to meeting the growing requirements placed on it, increased production, efficiency and reduced labour, along with the various political declarations and societal claims. Digitalisation is increasingly seen as a prerequisite to achieving the goal of sustainable agriculture and reduced labour. Therefore, it is the aim of the DiLaAg project to bring together scientists from different fields of expertise to work interdisciplinary on the complex topic of digitalisation in agriculture. In the DiLaAg consortium, PhD students and their supervisors from the university of Natural Resources and Life Sciences, vienna (BOKU), the university of technology vienna (TU) and the university of Veterinary Medicine vienna (VETMED) build the scientific core. The project was initiated and is funded by the Federal State of Lower Austria and the private foundation ‘Forum Morgen’. The PhD students work in the fields of robotics, image-based plant classification, agronomic analyses of crops, impact of technologies, digital twinning, livestock management on pasture and sustainability assessment as well as on interdisciplinary topics such as computer vision, application of digital sensor technologies, machine learning, complexity science, remote sensing, data integration, precision agriculture and precision livestock farming. Apart from this scientific nucleus, the DiLaAg consortium aims to act as a platform for innovation and exchange, by continuously expanding its network and collaborations in order to continue working on current challenges in agriculture. © ECPLF 2022. All rights reserved.

关键词： Sustainable development

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Elastic Smart Contracts in Blockchains

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IEEE/CAA Journal of Automatica Sinica 2021年第12期8卷 1901-1912页

作者： Schahram Dustdar Pablo Fernández JoséMaría García Antonio Ruiz-Cortés Distributed Systems Group Vienna University of TechnologyWien 1040Austria Smart Computer Systems Research and Engineering Lab Research Institute of Computer EngineeringUniversity of SevillaSevilla 41012Spain

In this paper,we deal with questions related to blockchains in complex Internet of Things(IoT)-based *** ecosystems are typically composed of IoT devices,edge devices,cloud computing software services,as well as people,who are decision makers in scenarios such as smart *** decisions related to analytics can be based on data coming from IoT sensors,software services,and ***,they are typically based on different levels of abstraction and *** poses a number of challenges when multiple blockchains are used together with smart *** work proposes to apply our concept of elasticity to smart contracts and thereby enabling analytics in and between multiple blockchains in the context of *** propose a reference architecture for Elastic Smart Contracts and evaluate the approach in a smart city scenario,discussing the benefits in terms of performance and self-adaptability of our solution.

关键词： Blockchain elasticity Internet of Things(IoT) smart cities smart contracts virtual chains

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