S. Bosse, D. Weiis, D. Schmidt, Supervised Distributed Multi-Instance and Unsupervised Single-Instance Autoencoder Machine Learning for Damage Diagnostics with High-Dimensional Data—A Hybrid Approach and Comparison Study, Computers 2021, 10(3), 34;
doi:10.3390/computers10030034 Paper PDFPublisher
Structural health monitoring (SHM) is a promising technique for in-service inspection of technical structures in a broad field of applications in order to reduce maintenance efforts as well as the overall structural weight. SHM is basically an inverse problem deriving physical properties like damages or material inhomogeneity (target features) from sensor data. Often models defining the relationship between predictable features and sensors are required but not available. The main objective of this work is the investigation of model-free Distributed Machine Learning (DML) for damage diagnostics under resource and failure constraints by using multi-instance ensemble and model fusion strategies and featuring improved scaling and stability compared with centralised single-instance approaches. The diagnostic system delivers two features: A binary damage classification (damaged or non-damaged) and an estimation of the spatial damage position in case of a damaged structure. The proposed damage diagnostics architecture should be able to be used in low-resource sensor networks with soft real-time capabilities. Two different machine learning methodologies and architectures are evaluated and compared posing low- and high-resolution sensor processing for low- and high-resolution damage diagnostics, i.e., a dedicated supervised trained low-resource and an unsupervised trained high-resource deep learning approach, respectively. In both architectures state-based recurrent Artificial Neural Networks are used that process spatially and time-resolved sensor data from experimental ultrasonic guided wave measurements of a hybrid material (carbon fibre laminate) plate with pseudo defects. Finally, both architectures can be fused to a hybrid architecture with improved damage detection accuracy and reliability. An extensive evaluation of the damage prediction by both systems shows high reliability and accuracy of damage detection and localisation, even by the distributed multi-instance architecture with a resolution in the order of the sensor distance.
S. Bosse, Distributed Serverless Chat Bot Networks using mobile Agents: A Distributed Data Base Model for Social Networking and Data Analytics, 13th International Conference on Agents and Artificial Intelligence (ICAART),
Online, Worldwide, 4-6-2.2021 Paper PDFPresentation VIDEOPrsentation SLIDESConference
Today human-machine dialogues performed and moderated by chat bots are ubiquitous. Commonly, centralised and server-based chat bot software is used to implement rule-based and intelligent dialogue robots. Furthermore, human networking is not supported. Rule-based chat bots typically implement an interface to a knowledge data base in a more natural way. The dialogue topics are narrowed and static. Intelligent chat bots aim to improve dialogues and conversational quality over time and user experience. In this work, mobile agents are used to implement a distributed, decentralised, serverless dialogue robot network that enables ad-hoc communication between humans and machines (networks) and between human groups via the chat bot network (supporting personalized and mass communication). I.e., the chat bot networks aims to extend the communication and social interaction range of humans, especially in mobile environments, by a distributed knowledge and data base approach. Additionally, the chat bot network is a sensor data acquisition and data aggregator system enabling large-scale crowd-based analytics. A first proof-of-concept demonstrator is shown identifying the challenges arising with self-organising distributed chat bot networks in resource-constrained mobile networks. The novelty of this work is a hybrid chat bot multi-agent architecture enabling scalable distributed and adaptive communicating chat bot networks.