308,944 research outputs found
On Generators and Congenerators
Get PDFThe question of the existence of generators and cogenerators
i n a category is of i n t e r e s t i n view of the special
adjoint functor theorem. ISBELL has given an example (unpublished)
which shows t h a t the existence of a cogenerator
i s a necessary part of the hypothesis of the special
adjoint functor theorem. This example also shows t h a t the
category of groups has no cogenerator. (Clearly the f r e e
group on one element i s a generator i n the category of
groups.) It is well known t h a t there e x i s t generators and
cogenerators i n the categories of commutative groups,
Comrnutative Lie algebras (over a f i e l d ) and commutative
r e s t r i c t e d Lie algebras, because a l l of these categories
are module categories. By ISBELL1s r e s u l t when one drops
the condition of cornmutativity for the category of commut
a t i v e groups there i s no longer a cogenerator. We have
Proved similar r e s u l t s for the categories of commutative
Lie algebras and commutative r e s t r i c t e d Lie algebras. The
r e s u l t s are summarized i n the l i s t below where we have
included some r e l a t e d categories
Structural features of triethylammonium acetate through molecular dynamics
Get PDFI have explored the structural features and the dynamics of triethylammonium acetate by means of semi-empirical (density functional tight binding, DFTB) molecular dynamics. I find that the results from the present simulations agree with recent experimental determinations with only few minor differences in the structural interpretation. A mixture of triethylamine and acetic acid does not form an ionic liquid, but gives rise to a very complex system where ionization is only a partial process affecting only few molecules (1 over 4 experimentally). I have also found that the few ionic couples are stable and remain mainly embedded inside the AcOH neutral moiety
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Tecniche di Machine Learning per Macchine Smart e Impianti Smart: Applicazioni in Manutenzione Predittiva e Condition Monitoring Industriale
No full textIl crescente interesse per le tecniche di intelligenza artificiale è notevolmente accresciuto nell’ultimo decennio tra tutti i settori industriali. Tali metodologie supportano lo sviluppo di nuove funzionalità attraverso l’integrazione di sistemi cyber-fisici e unità di monitoraggio integrate in macchine e impianti, come ricorda il prefisso smart. Il lavoro di tesi si concentra sul Condition Monitoring e la manutenzione predittiva, i cui compiti più diffusi riguardano l’Anomaly Detection, la Fault Classification e la stima della Remaining Useful Life. Molte applicazioni “industry-class” si affidano a metodi di Supervised Machine Learning, in quanto possiedono metriche di valutazione più mature dei metodi unsupervised. Nonostante ciò, limiti addizionali aggiungono complessità allo sviluppo di una soluzione commerciabile. Il flusso di lavoro perciò necessita di gestire restrizioni provenienti dalla piattaforma di monitoraggio, come il carico CPU sopportabile, o diverse condizioni operative. Tali limitazioni spesso sfociano in trade-off di progetto, ma tale informazione nota a priori può portare effetti benefici al flusso stesso. Il lavoro di tesi analizza le limitazioni più diffuse e il loro rapporto con il flusso di lavoro, fornendo alcuni metodi per integrare in modo benefico tale informazione a priori a seconda del compito di monitoraggio e del passo di elaborazione dati. Per validare i metodi proposti sono analizzati due casi di studio riguardanti l’impiego di umidificatori industriali. In Feature Engineering la selezione delle feature è uno dei fattori maggiormente impattanti in quanto la maggioranza delle risorse computazionali e di memoria è impiegata per la loro estrazione. Una Feature Selection sostenibile deve perciò considerarne i costi di estrazione e l’utilità in campo. A tale scopo è proposto un algoritmo chiamato Feature Voting per operare una selezione multi-obiettivo considerando dataset appartenenti a condizioni operative differenti e attributi delle feature, come i costi di estrazione. È inoltre introdotta un’ottimizzazione del Feature Voting basata sul Design Of Experiments. Il Feature Voting incrementa la portabilità di un’applicazione sviluppata in molti contesti operativi diversi, come auspicato dalla pratica industriale. È inoltre proposta una ridefinizione della funzione target, basata su classi di manutenzione condivise, allo scopo di efficientare la Fault Classification riducendo la complessità di classificazione. L’approccio è esteso considerando i costi di manutenzione e sviluppando adattamenti cost-sensitive dei classificatori attraverso l’Ensemble Learning e il paradigma client-server. La procedura riduce i costi di errata classificazione senza riallenare i classificatori sviluppati. Un successivo compito di post-elaborazione riguarda l’utilizzo di predizioni instabili fornite da un classificatore black-box sull’azione di manutenzione da eseguire. È perciò proposta una tecnica di stabilizzazione delle predizioni basata sulla teoria dei Fuzzy Set, incorporando inoltre un meccanismo di isteresi dinamica. Il sistema proposto ha stabilizzato le predizioni e anticipato la richiesta di manutenzione, anche in condizioni di elevata incertezza. Un secondo tema è inerente all’utilizzo della stima della Remaining Useful Life e dell’incertezza associata per attività di post-prognostica. Il metodo proposto utilizza il Functional Profile Modelling per modellare lo stress del sistema, è poi applicato un opportuno adattamento di scala per la correzione a posteriori della stima. Il metodo proposto rappresenta un approccio alternativo guidato dalle richieste industriali legate all’apprendimento sul campo e all'integrazione di informazioni a posteriori. L’intera dissertazione offre inoltre una metodologia di analisi per correlare un flusso di progetto basato sul Machine Learning con i temi di interesse industriale nel campo della manutenzione smart.The increasing interest in Artificial Intelligence techniques has risen in the last decade among all the industrial knowledge domains. Such methods support new advanced functionalities assisted by the integration of cyber-physical systems and monitoring units inside machines and plants, as recalled with the smart appellative. The thesis focuses on Condition Monitoring and Predictive Maintenance in which Anomaly Detection, Fault Classification, and Remaining Useful Life estimation are the most common tasks to solve. Most industry-class applications rely on Supervised Machine Learning techniques, as the evaluation metrics are more consolidated than unsupervised methods. However, additional restrictions add complexity to the development of a marketable solution. During the development of such algorithms, a regular Machine Learning workflow needs to manage additional constraints coming from the monitoring platform, like the available CPU, memory, or different field conditions. Such restrictions often lead to design trade-offs, but such information is prior known, and the design process can benefit from it. This thesis aims to analyze some of the most diffused constraints, along with the Machine Learning workflow, to provide some techniques that enable beneficially embedding such prior information according to the monitoring task and the processing step. To validate the methods, two industrial case studies are analyzed regarding the employment of industrial humidifiers. In Feature Engineering, the selection of features is one of the most impacting factors because many computational and memory resources, when using a deployed model, are due to the extraction of features. A sustainable Feature Selection thus needs to consider their extraction costs and their validity for a given field condition. Feature Selection is addressed by proposing an algorithm called Feature Voting to perform a multi-objective selection that considers datasets belonging to different field conditions and feature attributes, like the computational and memory extraction costs. Feature Voting tuning is also performed based on the Design Of Experiments. Feature Voting boosts the usage performance of a deployed machine in plenty of working environments, as found in the industrial practice. A maintenance-based target redefinition has been proposed to efficiently improve Fault Classification, i.e. grouping fault types according to shared maintenance interventions to lower the classification complexity. The approach is extended by considering the maintenance costs, leading to cost-sensitive adaptations of classifiers exploiting Ensemble Learning and the client-server paradigm. The procedure lowers the misclassification cost without retraining the deployed classifiers. In postprocessing, an additional task focuses on the practical use of the predictions given by a black-box unstable classifier on whether maintenance action to perform on the system. A prediction stabilization technique is proposed by exploiting the Fuzzy Set theory. A dynamical hysteresis mechanism is also introduced to increase the scheduling margin. The proposed system provided more stable predictions over time and anticipated the intervention alarm, even in highly uncertain conditions. A second postprocessing task deals with the post-prognostic usage of the Remaining Useful Life estimate and the management of its uncertainty. A method for the posterior correction of such an estimate is proposed. The technique exploits Functional Profile Modeling to describe the stress experienced by the monitored system on the field; then, a proper scaling is applied. The method represents an alternative approach, driven by the industrial requirement of learning from the field and integrating the extracted posterior information. All the thesis corpus offers an analysis methodology that relates a regular Machine Learning workflow with open issues of industrial interest in the smart maintenance field
Atomistic simulations of Imidazolium-based Ionic Liquids: current challenges for theoretical models.
No full textIn this communication we report the first X-Ray scattering study of imidazolium-based ionic liquids containing the bromide anion. The system studied was 1-octyl-3-metyl-imidazolium bromide ([C8mim]Br). The study was extended to the analogous salt, containing chloride as anion ([C8mim]Cl) which has been used for comparison. The measured diffraction patterns are compared with the theoretical spectra calculated from model geometries obtained with classical molecular dynamics simulations. The behavior and the performance of the available force fields in the description of bromide ion is discussed.
In particular, we shall show how the force employed in our simulations, which is an implementation of the one from Canogia-Lopes et al. (Lopes, J. N. C.; Deschamps, J.; Pádua, A. A. H. J. Phys Chem. B 2004, 108, 2038), is able to reproduce qualitatively all the prominent features of the measured spectra. This is a remarkable result given that the force field used in the present calculations is not specific to the title ILs, but is fully transferable to other systems.
Our calculations show, however, that the theoretical simulations employing that field present some serious discrepancy when compared to experiments at least in the case of the ILs considered here which are characterized by long side chains and by relatively less explored anions such as the Br−. In particular we have shown that the predicted density of the [C8mim]Br is 13% off the experimental value and that this effect can be related to features in the radial distribution function that, in fact, are partially corrected when carrying out the simulation at the volume corresponding to the experimental density. Other features that are bound to the linking of the Br− ion to the carbon ring remain very poorly described by the force field. The case of the [C8mim]Cl presents in general a better agreement between experiments and theory, but also in this case we have discrepancies especially for the long range part of the radial distributions. Simple modifications of the force field are currently under study in our laboratories and will be presented along with previous results
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An Acoustic Study of Bodo Vowels
Get PDFThis study investigates the acoustic features of the vowels of the eastern variety of Bodo. Data collected from 12 speakers in the field, reading a list of Bodo words revealed that Bodo has six unique vowel phonemes namely, , /i, u, e, ə, o, a/ where the vowel /ə/ is a higher than a normal mid, central vowel. While vowel F1 and F2 are distinct in Bodo, F3 does not play any role. The claims about Bodo vowels are further strengthened by normalized vowel plots and Euclidean distances calculated for vowel pairs
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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