A Deep Learning Approach To Vehicle Fault Detection Based On Vehicle Behavior

Vehicles and machinery play a crucial role in our daily lives, contributing to our transportationneeds and supporting various industries. As society strives for sustainability, the advancementof technology and efficient resource allocation become paramount. However, vehicle faultscontinue to pose a significant challenge, leading to accidents and unfortunate consequences.In this thesis, we aim to address this issue by exploring the effectiveness of an ensemble ofdeep learning models for supervised classification. Specifically, we propose to evaluate the performance of 1D-CNN-Bi-LSTM and 1D-CNN-Bi-GRU models. The Bi-LSTM and Bi-GRUmodels incorporate a multi-head attention mechanism to capture intricate patterns in the data.The methodology involves initial feature extraction using 1D-CNN, followed by learning thetemporal dependencies in the time series data using Bi-LSTM and Bi-GRU. These models aretrained and evaluated on a labeled dataset, yielding promising results. The successful completion of this thesis has met the objectives and scope of the research, and it also paves the way forfuture investigations and further research in this domain

A Deep Learning Approach To Vehicle Fault Detection Based On Vehicle Behavior

Vehicles and machinery play a crucial role in our daily lives, contributing to our transportationneeds and supporting various industries. As society strives for sustainability, the advancementof technology and efficient resource allocation become paramount. However, vehicle faultscontinue to pose a significant challenge, leading to accidents and unfortunate consequences.In this thesis, we aim to address this issue by exploring the effectiveness of an ensemble ofdeep learning models for supervised classification. Specifically, we propose to evaluate the performance of 1D-CNN-Bi-LSTM and 1D-CNN-Bi-GRU models. The Bi-LSTM and Bi-GRUmodels incorporate a multi-head attention mechanism to capture intricate patterns in the data.The methodology involves initial feature extraction using 1D-CNN, followed by learning thetemporal dependencies in the time series data using Bi-LSTM and Bi-GRU. These models aretrained and evaluated on a labeled dataset, yielding promising results. The successful completion of this thesis has met the objectives and scope of the research, and it also paves the way forfuture investigations and further research in this domain

Rehabilitation of Collective Housing Ensembles Made of Large Precast Panels - General Aspects

Reconsidering the attitude towards safe and energy efficient buildings makes that the forefront of a rehabilitation process be the ensuring a healthy environment, a certain quality of the indoor environment and as well as ensuring the safety of the inhabitants. The indoor environmental quality, a determining factor regarding the health and welfare of occupants of a building, is determined by the air composition (in reference to chemical pollutants, physical, biological or other nature) and by the comfort (with the main components, acoustic, thermal and visual)

A.: Using Textual and Visual Processing in Scalable Concept Image Annotation Challenge

Abstract. This paper describes UAIC 1 's system built for participating in the Scalable Concept Image Annotation challenge 2015. We submitted runs both for Subtask 1 (Image Concept detection and localisation) and for Subtask 2 (Generation of Textual Descriptions of Images). For the first subtask we created an ontology with relations between concepts and their synonyms, hyponyms and hypernyms and also with relations between concepts and related words. For the second subtask, we created a resource that contains triplets (concept1, verb, concept2), where concepts are from the list of concepts provided by the organizers and verb is a relation between concepts. With this resource we build sentences in which concept1 is subject, verb is predicate and concept2 is complement