10784 research outputs found
Sort by
Person Re-identification with Spatial Multi-granularity Feature Exploration for Social Risk Situational Assessment
No full textPublisher Copyright: © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.Recently, the “human-oriented” concept of security development has become a consensus among all countries. This depends mainly on intelligent surveillance systems that can support person re-identification (Re-ID) technology to empower social risk situational assessment applications. However, existing Re-ID methods mainly focus on single and fixed convolutional operations for feature extraction, ignoring the multi-dimensional spatial association of the human body, which limits the performance of Re-ID. Human cognition when identifying people does not solely rely on visual cues of the individual in sight, but also on his/her behavioral and gestural characteristics. To solve this issue and inspired by the aforementioned cognitive mechanism of the human brain, this study developed a spatial multi-granularity feature exploration (SMGFE) model for person Re-ID. The proposed SMGFE model comprises two main steps: (i) a multi-granularity feature exploration strategy and (ii) a human spatial association scheme. The former mainly includes coarse (original person images), medium (multi-regional divided person images), and fine-tuned (keypoints of the human body) level features, which form the multi-granularity feature representation. An undirected graph model was then developed to construct multi-dimensional spatial relations for each person. Finally, the unified optimization strategy was applied to train the framework to achieve promising accuracy. We evaluated the proposed algorithm on frequently used and benchmark person Re-ID datasets (Market-1501 and DukeMTMC-reID). The cumulative match curve (CMC) and mean average precision (mAP), which are the common measuring criteria for most person Re-ID methods reported to date, were used to verify the experimental results. Experiments show that our proposed algorithm achieved unrivaled performance levels. In addition, based on the spatial multi-granularity feature exploration strategy, the time efficiency of the proposed method for detecting specific instances can reach O(n), making it suitable for deployment in low-resource terminals for security risk assessment, including Android/iOS analysis servers, urban safety risk surveillance systems, and warning platforms for situational awareness.Peer reviewe
Communications and Data Science for the Success of Vehicle-to-Grid Technologies: Current State and Future Trends
Get PDFPublisher Copyright: © 2024 by the authors.Vehicle-to-grid (V2G) technology has emerged as a promising solution for enhancing the integration of electric vehicles (EVs) into the electric grid, offering benefits, such as distributed energy resource (DER) integration, grid stability support, and peak demand management, among others, as well as environmental advantages. This study provides a comprehensive review of V2G systems, with a specific focus on the role of the communication, as they have been identified as key enablers, as well as the challenges that V2G must face. It begins by introducing the fundamentals of V2G systems, including their architecture, operation, and a description of the benefits for different sectors. It then delves into the communication technologies and protocols in V2G systems, highlighting the key requirements in achieving reliable and efficient communication between EVs and the different agents involved. A comprehensive review of communication standards is described, as well as the main communication technologies, which are evaluated in terms of their suitability for V2G applications. Furthermore, the study discusses the challenges and environmental implications of V2G technology, emphasizing the importance of addressing strong and reliable communications to maximize its potential benefits. Finally, future research directions and potential solutions for overcoming challenges in V2G systems are outlined, offering useful insights for researchers, policymakers, and administrations as well as related industry stakeholders.Peer reviewe
A Decentralized Private Data Marketplace using Blockchain and Secure Multi-Party Computation
No full textPublisher Copyright: © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.Big data has proven to be a very useful tool for companies and users, but companies with larger datasets have ended being more competitive than the others thanks to machine learning or artificial intelligence. Secure multi-party computation (SMPC) allows the smaller companies to jointly train arbitrary models on their private data while assuring privacy, and thus gives data owners the ability to perform what are currently known as federated learning algorithms. Besides, with a blockchain it is possible to coordinate and audit those computations in a decentralized way.In this document, we consider a private data marketplace as a space where researchers and data owners meet to agree the use of private data for statistics or more complex model trainings. This document presents a candidate architecure for a private data marketplace by combining SMPC and a public, general-purpose blockchain. Such a marketplace is proposed as a smart contract deployed in the blockchain, while the privacy preserving computation is held by SMPC.Peer reviewe
Encoding contact size using static and dynamic electrotactile finger stimulation: natural decoding vs. trained cues
Get PDFPublisher Copyright: © The Author(s) 2024.Electrotactile stimulation through matrix electrodes is a promising technology to restore high-resolution tactile feedback in extended reality applications. One of the fundamental tactile effects that should be simulated is the change in the size of the contact between the finger and a virtual object. The present study investigated how participants perceive the increase of stimulation area when stimulating the index finger using static or dynamic (moving) stimuli produced by activating 1 to 6 electrode pads. To assess the ability to interpret the stimulation from the natural cues (natural decoding), without any prior training, the participants were instructed to draw the size of the stimulated area and identify the size difference when comparing two consecutive stimulations. To investigate if other “non-natural” cues can improve the size estimation, the participants were asked to enumerate the number of active pads following a training protocol. The results demonstrated that participants could perceive the change in size without prior training (e.g., the estimated area correlated with the stimulated area, p 80% success rate). However, natural decoding was also challenging, as the response area changed gradually and sometimes in complex patterns when increasing the number of active pads (e.g., four extra pads needed for the statistically significant difference). Nevertheless, by training the participants to utilize additional cues the limitations of natural perception could be compensated. After the training, the mismatch in the activated and estimated number of pads was less than one pad regardless of the stimulus size. Finally, introducing the movement of the stimulus substantially improved discrimination (e.g., 100% median success rate to recognize ≥ one-pad difference). The present study, therefore, provides insights into stimulation size perception, and practical guidelines on how to modulate pad activation to change the perceived size in static and dynamic scenarios.Peer reviewe
On the modeling of external mass transfer phenomena in Pd-based membrane separations
Get PDFPublisher Copyright: © 2024 The AuthorsIn this work, external mass transfer phenomena around hydrogen selective Pd-based membranes were analyzed experimentally and mathematically modelled. A supported Pd-Ag membrane was tested in pure hydrogen and in hydrogen/nitrogen mixtures using three different membrane lengths. Pressure, temperature, gas flow rate and feed composition were varied to obtain an elaborate dataset that could be used for analysis and modelling. Strong influences of concentration polarization and hydrogen depletion were observed. Various empirical correlations describing gas phase mass transfer around a tubular membrane from literature were tested, but none of them yielded a sufficiently accurate prediction of concentration polarization observed in the experiments. Therefore, a new Sherwood correlation was fitted using the dataset. The obtained correlation (Sh=1.846∙Gz0.60) showed significantly improved predictive behavior for the system used in this work and represents a potentially powerful tool for the modelling of membrane separators for pure hydrogen production.Peer reviewe
EVALUATION AND MANAGEMENT OF EXISTING INFRASTRUCTURES FROM A DECISION-MAKING PERSPECTIVE
No full textPeer reviewe
Brain state-dependent repetitive transcranial magnetic stimulation for motor stroke rehabilitation: a proof of concept randomized controlled trial
Get PDFPublisher Copyright: Copyright © 2024 Mahmoud, Baur, Zrenner, Brancaccio, Belardinelli, Ramos-Murguialday, Zrenner and Ziemann.Background: In healthy subjects, repetitive transcranial magnetic stimulation (rTMS) targeting the primary motor cortex (M1) demonstrated plasticity effects contingent on electroencephalography (EEG)-derived excitability states, defined by the phase of the ongoing sensorimotor μ-oscillation. The therapeutic potential of brain state-dependent rTMS in the rehabilitation of upper limb motor impairment post-stroke remains unexplored. Objective: Proof-of-concept trial to assess the efficacy of rTMS, synchronized to the sensorimotor μ-oscillation, in improving motor impairment and reducing upper-limb spasticity in stroke patients. Methods: We conducted a parallel group, randomized double-blind controlled trial in 30 chronic stroke patients (clinical trial registration number: NCT05005780). The experimental intervention group received EEG-triggered rTMS of the ipsilesional M1 [1,200 pulses; 0.33 Hz; 100% of the resting motor threshold (RMT)], while the control group received low-frequency rTMS of the contralesional motor cortex (1,200 pulses; 1 Hz, 115% RMT), i.e., an established treatment protocol. Both groups received 12 rTMS sessions (20 min, 3× per week, 4 weeks) followed by 50 min of physiotherapy. The primary outcome measure was the change in upper-extremity Fugl-Meyer assessment (FMA-UE) scores between baseline, immediately post-treatment and 3 months’ follow-up. Results: Both groups showed significant improvement in the primary outcome measure (FMA-UE) and the secondary outcome measures. This included the reduction in spasticity, measured objectively using the hand-held dynamometer, and enhanced motor function as measured by the Wolf Motor Function Test (WMFT). There were no significant differences between the groups in any of the outcome measures. Conclusion: The application of brain state-dependent rTMS for rehabilitation in chronic stroke patients is feasible. This pilot study demonstrated that the brain oscillation-synchronized rTMS protocol produced beneficial effects on motor impairment, motor function and spasticity that were comparable to those observed with an established therapeutic rTMS protocol. Clinical Trial Registration: ClinicalTrials.gov, identifier [NCT05005780].Peer reviewe
Study of a New Novel HVOAF Coating Based on a New Multicomponent Al80Mg10Si5Cu5 Alloy
Get PDFPublisher Copyright: © 2024 by the authors.This paper presents and demonstrates the development of a new lightweight coating for aluminum alloy from a novel multicomponent alloy based on the AlSiMgCu system. The coating was applied using a newly designed approach that combined high velocity oxy-fuel (HVOF) and plasma spraying processes. This hybrid technique enables the deposition of coatings with enhanced performance characteristics. The optical microscopy (OM) and scanning electron microscopy with energy dispersive X-ray spectroscopy (SEM + EDS) revealed a strong adhesion and compaction between the multicomponent coating and the A6061 substrate. The new coating improved hardness by 50% and increased electrical conductivity by approximately 3.3 times compared to the as-cast alloy. Corrosion tests showed a lower corrosion rate, comparable to thermally treated A6061 alloy. Tribological tests indicated over 20% reduction in friction and over 50% reduction in wear rate. This suggests that multicomponent aluminum coatings could improve automotive and parts in contact with hydrogen by enhancing hydrogen fragilization resistance, corrosion resistance, electrical conductivity, and wear properties, with further optimization of thermal spraying potentially boosting performance even further.Peer reviewe
Leveraging Local Digital Twins for planning age-friendly urban environments
Get PDFPublisher Copyright: © 2024 The AuthorsIn an era of rapid urbanisation and an ageing population, innovative urban planning paradigms and tools are essential for creating inclusive, safe, resilient, and sustainable cities. Moreover, in the digital age, untapped potential exists for using disruptive technologies in urban planning to enhance evidence-based decision-making. This study explored the promotion of age-friendly environments through the transformative potential of Local Digital Twins (LDTs) by integrating geographic information system (GIS) data, data analytics, and artificial intelligence. Tested in the European Commission-funded URBANAGE project, this study presents a digital twin-based Long-Term Urban Planner tool with simulation capabilities that allow for a comprehensive analysis and modelling of the effects of urban interventions. Two use cases are showcased: one suggesting public space intervention and the other tackling future demographic trends. The main contribution of this study is the definition and development of an LDT using a modular-component-based approach that facilitates reuse and adaptation. Unlike isolated approaches, it provides a holistic solution that integrates social and technological domains. This study advances the understanding of the use of LDTs to create inclusive neighbourhoods by assessing neighbourhood age-friendliness and proposing informed urban interventions while underscoring the importance of robust data governance and capacity building among civil servants.Peer reviewe
Targeting the high frequency tail of wave spectra for energy harvesting in marine sensor networks
No full textPublisher Copyright: © 2024 Elsevier LtdWhile the conventional philosophy of wave energy conversion is to target the large amounts of power in the peak of the input wave spectrum, this study proposes that for the application of powering marine sensor networks (MSNs), it is advantageous to target the high frequency tail of the wave spectrum. This strategy is predicated on two primary advantages: the spatial and temporal persistence of the wave energy resource in the high-frequency region and its compatibility with the resonance characteristics of smaller MSN devices. To identify the optimal frequency range for energy harvesting, we conducted a detailed analysis of wave spectra across multiple coastal locations. This involved calculating and comparing the power spectra at different frequencies, using data from long-term wave measurements. The high-frequency tail was defined by determining the frequency above which the energy content showed consistent temporal and spatial stability across all study sites. The quantification of available power was achieved by integrating the wave power spectrum over the identified frequency range. Our case study, focusing on the coast of Queensland, Australia, reveals that frequencies above 2.5 rad/s consistently offer a stable and persistent energy resource. The available power in this range is quantified, totalling an average of 60 W/m, with additional analysis provided within narrower sub-bandwidths to address the inherent narrow-bandedness of wave energy harvesters. This research provides critical insights for the design of efficient wave energy harvesters tailored to the needs of diverse marine environments.Peer reviewe