Demand pattern analysis of taxi trip data for anomalies detection and explanation

Novi Zakon o obveznim odnosima promijenio je naziv instituta bankarske garancije u bankarsko jamstvo i pojam tog instituta izložen u čl. 1039. st. 1. i 2. (tako da se sada pod nazivom bankarskog jamstva pojavljuje samostalna bankarska garancija), dok ostale odredbe ranijeg ZOO-a sadržajno nisu promijenjene. Bankovna garancija jeste samostalna obveza banke garanta koja je akcesorna obveza jamca. Banka garant ne osigurava ispunjenje obveze glavnog dužnika, naprotiv, obvezuje se korisniku garancije nadoknaditi štetu, odnosno izvršiti obvezu koju u ugovorenom roku nije izvršio glavni dužnik. U radu izlažem pitanja u svezi s oblikom i vrstama garancije, kvalifikacijom i nastankom bančine obveze prema korisniku, pretpostavkama isplate, prenosivošću i potvrdom garancije kao i njihovoj zlouporabi.The new Law of mandatory relations has changed the name of the bank warranty to bank assurance and complete connotation is represented in article 1039. in section 1 and 2. (according to which, under the name of bank warranty is independent bank assurance) while other provisions from the Law of mandatory relations have not been significantly contextually changed. Bank warranty is independent obligation of the warrant bank, which is accessory obligation of the guarantor. Warrant bank does not assure implementation of the main debtor’s obligation, but it commits to compensate potential detriment towards the warranty user, in the other words, implement the obligation which has not been realized by the main debtor in specified time period

Novelty Detection with Autoencoders for System Health Monitoring in Industrial Environments

Predictive Maintenance (PdM) is the newest strategy for maintenance management in industrial contexts. It aims to predict the occurrence of a failure to minimize unexpected downtimes and maximize the useful life of components. In data-driven approaches, PdM makes use of Machine Learning (ML) algorithms to extract relevant features from signals, identify and classify possible faults (diagnostics), and predict the components’ remaining useful life (prognostics). The major challenge lies in the high complexity of industrial plants, where both operational conditions change over time and a large number of unknown modes occur. A solution to this problem is offered by novelty detection, where a representation of the machinery normal operating state is learned and compared with online measurements to identify new operating conditions. In this paper, a systematic study of autoencoder-based methods for novelty detection is conducted. We introduce an architecture template, which includes a classification layer to detect and separate the operative conditions, and a localizer for identifying the most influencing signals. Four implementations, with different deep learning models, are described and used to evaluate the approach on data collected from a test rig. The evaluation shows the effectiveness of the architecture and that the autoencoders outperform the current baselines