Hysteretic active control of base-isolated buildings

In this work, an active control law for base-isolated buildings is proposed. The crucial idea comes from the observation that passive base-isolation systems are hysteretic. Thus, an hysteretic active control strategy is designed in a way that the control force is smooth and limited by a prescribed bound. Furthermore, given a specific actuator with a physically limited maximum force and maximum rate of change, it is proven that the design parameters in the contributed control law can be chosen such that the control signal inherently satisfies the actuator constraints. Eight different ground-acceleration time-history records and a model of a 5-story building are used to study and compare the performance of a passive pure friction damper alone, with the addition of the proposed active control. Numerical analysis demonstrates that our control strategy effectively mitigates base displacement and shear without an increase in superstructure drift or acceleration.Peer ReviewedPostprint (author's final draft

Hysteresis based vibration control of base-isolated structures

An active control strategy for base-isolated structures is proposed in this work. The key idea comes from the observation that passive base isolation systems are hysteretic. Thus, an hysteresis based vibration control is designed in a way that the control force is smooth and limited by a prescribed bound. A model of a three-story building is used to study and compare the efficacy of a passive pure friction damper alone, with the addition of the proposed active control. We introduce a rate limiter to the actuator to simulate its limited speed capacity, present in every physical actuator. Simulations demonstrate that our active control strategy significantly reduces base displacements and shears without an increase in drift or accelerations.Peer ReviewedPostprint (published version

Hysteresis based vibration control of base-isolated structures

An active control strategy for base-isolated structures is proposed in this work. The key idea comes from the observation that passive base isolation systems are hysteretic. Thus, an hysteresis based vibration control is designed in a way that the control force is smooth and limited by a prescribed bound. A model of a three-story building is used to study and compare the efficacy of a passive pure friction damper alone, with the addition of the proposed active control. We introduce a rate limiter to the actuator to simulate its limited speed capacity, present in every physical actuator. Simulations demonstrate that our active control strategy significantly reduces base displacements and shears without an increase in drift or accelerations.Peer ReviewedPostprint (published version

Cordage, basketry and containers at the Pleistocene-Holocene boundary in southwest Europe. Evidence from Coves de Santa Maira (Valencian region, Spain)

In this study we present evidence of braided plant fibres and basketry imprints on clay recovered from Coves de Santa Maira, a Palaeolithic-Mesolithic cave site located in the Mediterranean region of Spain. The anatomical features of these organic fibre remains were identified in the archaeological material and compared with modern Stipa tenacissima (esparto grass). Based on direct dating, the fragments of esparto cord from our site are the oldest worked plant fibres in Europe. Sixty fragments of fired clay are described. The clay impressions have allowed us to discuss the making of baskets and containers. According to their attributes and their functional interpretation, we have grouped them into five types within two broad categories, hearth plates and baskets or containers. The clay pieces identified as fragments of containers with basketry impressions are less common than those of hearth plate remains and they are concentrated in the Epipalaeolithic occupation material (13.2-10.2 ka cal bp). The clay impressions from Santa Maira indicate that some fibres were treated or flattened, a preparation process that is known from historical and ethnological sources

Feeding habits of the Pine Marten (Martes martes L.) in Andratx (Mallorca, Balearic Islands)

Abstract not availabl

Time-based self-supervised learning for Wireless Capsule Endoscopy

State-of-the-art machine learning models, and especially deep learning ones, are significantly data-hungry; they require vast amounts of manually labeled samples to function correctly. However, in most medical imaging fields, obtaining said data can be challenging. Not only the volume of data is a problem, but also the imbalances within its classes; it is common to have many more images of healthy patients than of those with pathology. Computer-aided diagnostic systems suffer from these issues, usually over-designing their models to perform accurately. This work proposes using self-supervised learning for wireless endoscopy videos by introducing a custom-tailored method that does not initially need labels or appropriate balance. We prove that using the inferred inherent structure learned by our method, extracted from the temporal axis, improves the detection rate on several domain-specific applications even under severe imbalance. State-of-the-art results are achieved in polyp detection, with 95.00 ± 2.09% Area Under the Curve, and 92.77 ± 1.20% accuracy in the CAD-CAP dataset

Generic Feature Learning for Wireless Capsule Endoscopy Analysis

The interpretation and analysis of wireless capsule endoscopy (WCE) recordings is a complex task which requires sophisticated computer aided decision (CAD) systems to help physicians with video screening and, finally, with the diagnosis. Most CAD systems used in capsule endoscopy share a common system design, but use very different image and video representations. As a result, each time a new clinical application of WCE appears, a new CAD system has to be designed from the scratch. This makes the design of new CAD systems very time consuming. Therefore, in this paper we introduce a system for small intestine motility characterization, based on Deep Convolutional Neural Networks, which circumvents the laborious step of designing specific features for individual motility events. Experimental results show the superiority of the learned features over alternative classifiers constructed using state-of-the-art handcrafted features. In particular, it reaches a mean classification accuracy of 96% for six intestinal motility events, outperforming the other classifiers by a large margin (a 14% relative performance increase)

Recerca i gestió dins l’àmbit cinegètic a mode d’introducció

Abstract not availabl