Improving spare part search for maintenance services using topic modelling

To support the decision-making process in various industrial applications, many companies use knowledge management and Information Retrieval (IR). In an industrial setting, knowledge is extracted from data that is often stored in a semi-structured or unstructured format. As a result, Natural Language Processing (NLP) methods have been applied to a number of IR steps. In this work, we explore how NLP and particularly topic modelling can be used to improve the relevance of spare part retrieval in the context of maintenance services. A proposed methodology extracts topics from short maintenance service reports that also include part replacement data. An intuition behind the proposed methodology is that every topic should represent a specific root cause. Experimental were conducted for an ad-hoc retrieval system of service case descriptions and spare parts. The results have shown that our modification improves a baseline system thus boosting the performance of maintenance service solution recommendation.</p

Towards adaptive control in smart homes: Overall system design and initial evaluation of activity recognition

This paper proposes an approach for adaptive control over devices within a smart home, by learning user behavior and preferences over time. The proposed solution leverages three components: activity recognition for realising the state of a user, ontologies for finding relevant devices within a smart home, and machine learning for decision making. In this paper, the focus is on the first component. Existing algorithms for activity recognition are systematically evaluated on a real-world dataset. A thorough analysis of the algorithms’ accuracy is presented, with focus on the structure of the selected dataset. Finally, further study of the dataset is carried out, aiming at reasoning factors that influence the activity recognition performance

Efficient reprogramming of sensor networks using incremental updates and data compression

Reprogramming is an important issue in wireless sensor networks. It enables users to extend or correct functionality of a sensor network after deployment, at a low cost. In this paper, we investigate two problems: improving the energy efficiency and improving the delay of reprogramming. As enabling technologies we use data compression and incremental updates. We analyze different algorithms for both approaches, as well as their combination, when applied to resource-constrained devices. All algorithms are ported to the Contiki embedded operating system, and profiled for different types of reprogramming. Our results show that there is a clear trade-off between performance and resource requirements. Either VCDIFF, or the combination of Lempel-Ziv-77 or FastLZ compression algorithms with BSDIFF for delta encoding, have the best overall performance compared to other compression algorithms

Stripping und Analytik von Methan aus natuerlichen Waessern

TIB Hannover: DR 6053 / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekSIGLEDEGerman

Improving spare part search for maintenance services using topic modelling

To support the decision-making process in various industrial applications, many companies use knowledge management and Information Retrieval (IR). In an industrial setting, knowledge is extracted from data that is often stored in a semi-structured or unstructured format. As a result, Natural Language Processing (NLP) methods have been applied to a number of IR steps. In this work, we explore how NLP and particularly topic modelling can be used to improve the relevance of spare part retrieval in the context of maintenance services. A proposed methodology extracts topics from short maintenance service reports that also include part replacement data. An intuition behind the proposed methodology is that every topic should represent a specific root cause. Experimental were conducted for an ad-hoc retrieval system of service case descriptions and spare parts. The results have shown that our modification improves a baseline system thus boosting the performance of maintenance service solution recommendation

Context based service discovery in unmanaged networks using MDNS/DNS-SD

We propose an extension of the mDNS/DNS-SD service discovery protocol, which enables service clients to discover and select services based on their context. The extension improves scalability in large networks, which is of particular importance in future Internet of Things deployments

Improving the performance of trickle-based data dissemination in low-power networks

Trickle is a polite gossip algorithm for managing communication traffic. It is of particular interest in low-power wireless networks for reducing the amount of control traffic, as in routing protocols (RPL), or reducing network congestion, as in multicast protocols (MPL). Trickle is used at the network or application level, and relies on up-to-date information on the activity of neighbors. This makes it vulnerable to interference from the media access control layer, which we explore in this paper. We present several scenarios how the MAC layer in low-power radios violates Trickle timing. As a case study, we analyze the impact of CSMA/CA with ContikiMAC on Trickle’s performance. Additionally, we propose a solution called Cleansing that resolves these issues