145 research outputs found
Fault-Diagnosis of Grid Structures
No full textTheprobl] offaul diagnosis in grid-connected systems is considered. A diagnosisalnosisFI caln DAGS and based on the PMCmodel is presented. DAGS provides a diagnosis which is shown to be correct, alrect, possibl incomplB[I if thecardinalq; of theactual faul set isbelB a bound T # , dependent of theactual syndrome #. A bound T independent of # is alF derived by a worst-caseanalt-c covering the cases oftriangul;; square,hexagonal and octagonal grids. T is shown to be #(n ), where n is the size of the system, for al the grids considered. c 2002El2FB#I Science B.V.Al rights reserved. Keywords: FauleDFBVU#IFqV Faul diagnosis;System-l;FV diagnosis;Paralos architectures 1. I363S-222 Faul diagnosis is of primary importance to provide highdependabilBk incomplE systems. It aims at identifying the(faul] ornon-faulB] state of the units composing a system. Upon identi#cation,fault units may be eitherreplrF; orisolEIB from the rest of the system, andfaul recovery or recon#guration techniques may be used to restore a coherent state,alte,FB the system to resume operation,possibl with reduced performance (graceful degradation). System-l-T( diagnosis was introduced by Preparata et al [14] and has beendeepl investigated in lBI#]VFqBE It aims at diagnosing systems composed by units(usualE processors), with the requirement that they are abl to test each other by exchanging information through point-to-pointbi-directional li-di A system is represented by the system graph G=(N; L), an undirected graph where node set N represents units and # Corresponding author. Instituto diElB;;BFq;E] del;;BFq;E]BFl; del CNR, Via S. Maria 46, 56126, Pisa,ItalB E-mail address: [email protected] (S. Chessa). 0304-3975/02/$ - see front matter c 2002El2FVVU Science B.V.Al rights reserved. PII: S0304-3975(..
Unsupervised human process discovery in smart homes
No full textThe advances in the Internet of Things (IoT) have enabled the automation of various tasks like switching on the heating at home from work, seeing who is at your front door from the couch, supporting nurses in elderly homes, or the efficient delivery of packages. By enabling the connection between the physical and digital worlds, the IoT has shown how environments can be augmented with technology to enhance their capabilities, making them more intelligent, responsive, and adaptive. This widespread adoption of embedded systems turned pervasive (or ubiquitous) computing into reality: while sensors gather real-time data about the environment, actuators are used to automate the execution of many tasks that help the users of such environments. These environments, referred to as smart environments or smart spaces, represent an emerging class of IoT-based applications and are centered on their human users. Among smart spaces, smart homes and offices are representative examples. The goal is to enhance the quality of life, improve productivity, and provide personalized services by understanding and responding to the needs and preferences of the users, realizing the paradigm known as Ambient Intelligence (AmI). The literature presents various definitions of AmI systems and a set of distinct features that characterize them: sensitivity, responsiveness, adaptivity, ubiquity, and transparency. Sensitivity pertains to the AmI system's ability to perceive and comprehend the surrounding environment and its interaction context. Responsiveness and adaptivity, closely tied to sensitivity, indicate the system's capacity to promptly react, either proactively or reactively, to changes in the context in accordance with user preferences. Collectively, sensitivity, responsiveness, and adaptivity contribute to the overarching concept of context awareness. Lastly, the terms ubiquity and transparency directly relate to the idea of pervasive computing. Smart environments process and analyze the data collected from sensors to extract meaningful information. In this context, AmI is realized by utilizing techniques such as machine learning, artificial intelligence, and human-computer interaction (HCI). The rich data automatically collected via IoT sensors in smart spaces is used to get insights about the human behavior of the user (e.g., sleep tracking) or to perform automated actions for the user (e.g., automatically opening the blinds). For instance, current applications of human behavior monitoring in smart spaces include smart thermostats (e.g., Google Nest Learning Thermostat) and ambient assisted living (e.g., elderly fall detection systems). Modeling human activities and habits is not a simple task, due to the flexible and unstructured nature of human behavior. Recently, although it is still difficult to represent them following a precise flow of tasks, approaches have been proposed that model human habits as workflows. In particular, the research community and manufacturers have shown a great interest in applying process mining (PM) to smart spaces. Process mining is a fairly recent research discipline that combines data mining techniques with techniques used in Business Process Management (BPM), such as process modeling and process analysis. Process mining aims to extract, monitor, and improve processes based on real-world data. In particular, process discovery is a process mining technique used to discover and generate the process model describing the underlying behavior shown in the event log. The mined process model can be visualized in different forms, such as Petri nets, process flowcharts, or BPMN diagrams. Visualization helps to understand the structure and dynamics of processes within the smart space. However, even though process models could be extracted from smart space data, multiple important challenges arose. This thesis presents an overview of how some of the aforementioned research challenges are handled and to what degree they are addressed by the author
A Beacon in the Dark: Canakinumab. A New Therapeutic Perspective in Chronic Tophaceous Gout
No full textGout is the most common form of arthritis in adults. It is often associated with other comorbidities, which contraindicate the use of conventional therapies. The discovery of the role of interleukin-1β (IL-1β) in orchestrating the monosodium urate crystal-induced inflammatory response offered new therapeutic prospects to refractory patients, or to those in whom standard therapies are contraindicated. This paper describes a clinical case of a 65-year-old man with chronic tophaceous gouty arthropathy and subintrant flares, who had comorbidities contraindicating the use of conventional gout therapies-to which he did not respond-who was treated with canakinumab, a monoclonal selective inhibitor of IL-1β. The patient reported a gradual, rapid, and significant reduction in pain, with a response observed within 12 h of the administration of the drug. Consistent with previous clinical studies, canakinumab appeared to be a viable, safe, and effective alternative to conventional therapies in this patient with gout who had limited therapeutic options
Perspectivas de género en la traducción de la obra de las mujeres poetas de las Vanguardias españolas
No full textThe main aim of this research is to provide an analysis of the Italian translation of the poetry of a group of female authors of the Spanish Avant-garde movements, combining the theory of poetic translation with gender studies in translation. The translated poems that I analyze in this thesis are my own translations of some of Concha Méndez's, Ernestina de Champourcin's, Josefina de la Torre's, Rosa Chacel's, Cristina de Arteaga's and Lucía Sánchez Saornil’s works.The stated goal is to find a middle way between the radical strategies outlined by the Canadian feminist movement during the Eighties (cf. Diaz- Diocaretz 1985, Chamberlain 1988, 1998 , 1990 Godard, de Lotbiniere-Harwood 1991, Von Flotow 1991,1997, Simons 1996), and the translation techniques that are required to recreate the features of a poem without betraying the style and the aesthetics of the author (cf. Holmes 1969, 1978, Lefevere techniques 1975, Popovic 1976 Beaugrande 1978, Etkind 1982, Raffel 1988). My translation practice demonstrates that in order to translatein the feminineit is necessary to take a step further and introduce other important features that contribute to distinguish female discourse. Moreover, I will analyse the perspectives opened by the great feminist challenge in translation, and on the role of literature and translation in the rise of a new sensitivity in the female question
Wastewater Quality Indicator Estimation Using Machine Learning and Data Augmentation Techniques
No full textWe propose a novel design methodology for the estimation of the quality of wastewater using Ultraviolet Visible (UV-Vis) spectroscopy and Machine Learning. Addressing the challenge posed by limited real-world data, particularly in highly polluted industrial environments, this study introduces a data augmentation method based on Conditional Generative Adversarial Networks (CGAN). The effectiveness of this method is evaluated by creating a regression model based on a Multi-layer Perceptron (MLP) to estimate the chemical oxygen demand, a water quality indicator, using the UV-Vis absorption spectrum. The proposed method demonstrates that insufficient wastewater sample data can be augmented to improve the performance of the regression task for chemical oxygen demand (COD) estimation
Multitarget Wastewater Quality Assessment in a Smart Industry Context
No full textThis study addresses the need for a rapid and accurate process monitoring by developing an innovative approach for wastewater quality assessment to enhance the Industry 4.0’s vision. By integrating Ultraviolet-Visible (UV-Vis) spectroscopy with Machine Learning (ML), we focus on accurately determine key indicators such as Chemical Oxygen Demand, Total Suspended Solids (TSS), chlorides, and conductivity. Our findings demonstrate the efficacy of ML models in accurately predicting water quality from UV-Vis spectral data, underscoring their potential for real-time monitoring and analysis in industrial settings. The study also revealed the potential for both single and multitarget predictions. Additionally, the feature importance analysis provided valuable insights into the spectral regions most relevant for predicting each water quality indicator. This approach aligns with the goals of Industry 4.0, offering a smart, efficient solution for environmental monitoring and sustainable resource management
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