Sökning: "Anomaly detection"

Visar resultat 41 - 45 av 81 avhandlingar innehållade orden Anomaly detection.

1. 41. Image based methods for nondestructive detection of compression wood in sawn timber

   Författare :Jan Nyström; Luleå tekniska universitet; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Träteknik; Wood Technology;

   Sammanfattning : Compression wood is an anomaly in wood formed by the living softwood tree to compensate for excessive, asymmetric loads by developing special fibres. Compression wood has special properties that are unwanted in the utilisation of sawn wood products and is thus desirable to detect and reject in an early stage. LÄS MER

3. 42. Automated Squat Detection in Railway Switches & Crossings Using Vibration Measurements and Machine Learning

   Författare :Yang Zuo; Matti Rantatalo; Johan Odelius; Florian Thiery; Praneeth Chandran; Taoufik Najeh; João Carlos Elói De Jesus Pombo; Luleå tekniska universitet; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Operation and Maintenance Engineering; Drift och underhållsteknik;

   Sammanfattning : Railway switches and crossings (S&Cs) play a vital role in the operational integrity of a railway network, and their malfunctioning poses substantial risks such as traffic disruptions, economic ramifications, and potential life-threatening accidents. Consequently, it becomes imperative to proactively identify potential defects in S&Cs at an early stage and maintain continuous monitoring of their condition to avoid adverse consequences. LÄS MER

4. 43. Robust and Efficient Federated Learning for IoT Security

   Författare :Han Wang; Shahid Raza; György Dán; Uppsala universitet; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Internet of Things; Federated Learning; Machine Learning; Intrusion Detection System; Communication Efficiency; Robustness; Adversarial AI; Device Fingerprinting; Device Identification; Cyber Threat Intelligence; Computer Science with specialization in Computer Communication; Datavetenskap med inriktning mot datorkommunikation;

   Sammanfattning : The widespread adoption of Internet of Things (IoT) devices has led to substantial progress across various industrial sectors, including healthcare, transportation, and manufacturing. However, these devices also introduce significant security vulnerabilities because they are often deployed without adequate security measures, making them susceptible to cyber threats. LÄS MER

5. 44. Towards safe and efficient application of deep neural networks in resource-constrained real-time embedded systems

   Författare :Siyu Luan; Zonghua Gu; Leonid B. Freidovich; Lei Feng; Umeå universitet; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Machine Learning Deep Learning; Real-Time Embedded systems; Out-of-Distribution Detection; Distribution Shifts; Deep Reinforcement Learning; Model Compression; Policy Distillation.;

   Sammanfattning : We consider real-time safety-critical systems that feature closed-loop interactions between the embedded computing system and the physical environment with a sense-compute-actuate feedback loop. Deep Learning (DL) with Deep Neural Networks (DNNs) has achieved success in many application domains, but there are still significant challenges in its application in real-time safety-critical systems that require high levels of safety certification under significant hardware resource constraints. LÄS MER

7. 45. Big Data Analytics for eMaintenance : Modeling of high-dimensional data streams

   Författare :Liangwei Zhang; Wolfgang Birk; Luleå tekniska universitet; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Drift och underhållsteknik; Operation and Maintenance Engineering;

   Sammanfattning : Big Data analytics has attracted intense interest from both academia and industry recently for its attempt to extract information, knowledge and wisdom from Big Data. In industry, with the development of sensor technology and Information & Communication Technologies (ICT), reams of high-dimensional data streams are being collected and curated by enterprises to support their decision-making. LÄS MER