5 research outputs found
Adaptive forgetting factor fictitious play
It is now well known that decentralised optimisation can be formulated as a potential game, and game-theoretical learning algorithms can be used to find an optimum. One of the most common learning techniques in game theory is fictitious play. However fictitious play is founded on an implicit assumption that opponents' strategies are stationary. We present a novel variation of fictitious play that allows the use of a more realistic model of opponent strategy. It uses a heuristic approach, from the online streaming data literature, to adaptively update the weights assigned to recently observed actions. We compare the results of the proposed algorithm with those of stochastic and geometric fictitious play in a simple strategic form game, a vehicle target assignment game and a disaster management problem. In all the tests the rate of convergence of the proposed algorithm was similar or better than the variations of fictitious play we compared it with. The new algorithm therefore improves the performance of game-theoretical learning in decentralised optimisation
Tool condition monitoring of diamond-coated burrs with acoustic emission utilising machine learning methods
Within manufacturing there is a growing need for autonomous Tool Condition Monitoring (TCM) systems, with the ability to predict tool wear and failure. This need is increased, when using specialised tools such as Diamond-Coated Burrs (DCBs), in which the random nature of the tool and inconsistent manufacturing methods create large variance in tool life. This unpredictable nature leads to a significant fraction of a DCB toolâs life being underutilised due to premature replacement. Acoustic Emission (AE) in conjunction with Machine Learning (ML) models presents a possible on-machine monitoring technique which could be used as a prediction method for DCB wear. Four wear life tests were conducted with a â
1.3 mm #1000 DCB until failure, in which AE was continuously acquired during grinding passes, followed by surface measurements of the DCB. Three ML model architectures were trained on AE features to predict DCB mean radius, an indicator of overall tool wear. All architectures showed potential of learning from the dataset, with Long Short-Term Memory (LSTM) models performing the best, resulting in prediction error of MSE = 0.559 ÎŒm2 after optimisation. Additionally, links between AE kurtosis and the toolâs run-out/form error were identified during an initial review of the data, showing potential for future work to focus on grinding effectiveness as well as overall wear. This paper has shown that AE contains sufficient information to enable on-machine monitoring of DCBs during the grinding process. ML models have been shown to be sufficiently precise in predicting overall DCB wear and have the potential of interpreting grinding condition
The neurobiology of cortical music representations
Music is undeniable one of humanityâs defining traits, as it has been documented since the earliest
days of mankind, is present in all knowcultures and perceivable by all humans nearly alike.
Intrigued by its omnipresence, researchers of all disciplines started the investigation of musicâs
mystical relationship and tremendous significance to humankind already several hundred
years ago. Since comparably recently, the immense advancement of neuroscientific methods
also enabled the examination of cognitive processes related to the processing of music. Within
this neuroscience ofmusic, the vast majority of research work focused on how music, as an auditory
stimulus, reaches the brain and howit is initially processed, aswell as on the tremendous
effects it has on and can evoke through the human brain. However, intermediate steps, that is
how the human brain achieves a transformation of incoming signals to a seemingly specialized
and abstract representation of music have received less attention. Aiming to address this gap,
the here presented thesis targeted these transformations, their possibly underlying processes
and how both could potentially be explained through computational models. To this end, four
projects were conducted. The first two comprised the creation and implementation of two
open source toolboxes to first, tackle problems inherent to auditory neuroscience, thus also affecting
neuroscientific music research and second, provide the basis for further advancements
through standardization and automation. More precisely, this entailed deteriorated hearing
thresholds and abilities in MRI settings and the aggravated localization and parcellation of the
human auditory cortex as the core structure involved in auditory processing. The third project
focused on the humanâs brain apparent tuning to music by investigating functional and organizational
principles of the auditory cortex and network with regard to the processing of different
auditory categories of comparable social importance, more precisely if the perception of music
evokes a is distinct and specialized pattern. In order to provide an in depth characterization
of the respective patterns, both the segregation and integration of auditory cortex regions was
examined. In the fourth and final project, a highly multimodal approach that included fMRI,
EEG, behavior and models of varying complexity was utilized to evaluate how the aforementioned
music representations are generated along the cortical hierarchy of auditory processing
and how they are influenced by bottom-up and top-down processes. The results of project 1
and 2 demonstrated the necessity for the further advancement of MRI settings and definition
of working models of the auditory cortex, as hearing thresholds and abilities seem to vary as
a function of the used data acquisition protocol and the localization and parcellation of the
human auditory cortex diverges drastically based on the approach it is based one. Project 3
revealed that the human brain apparently is indeed tuned for music by means of a specialized
representation, as it evoked a bilateral network with a right hemispheric weight that was not
observed for the other included categories. The result of this specialized and hierarchical recruitment
of anterior and posterior auditory cortex regions was an abstract music component
ix
x SUMMARY
that is situated in anterior regions of the superior temporal gyrus and preferably encodes music,
regardless of sung or instrumental. The outcomes of project 4 indicated that even though
the entire auditory cortex, again with a right hemispheric weight, is involved in the complex
processing of music in particular, anterior regions yielded an abstract representation that varied
excessively over time and could not sufficiently explained by any of the tested models. The
specialized and abstract properties of this representation was furthermore underlined by the
predictive ability of the tested models, as models that were either based on high level features
such as behavioral representations and concepts or complex acoustic features always outperformed
models based on single or simpler acoustic features. Additionally, factors know to influence
auditory and thus music processing, like musical training apparently did not alter the
observed representations. Together, the results of the projects suggest that the specialized and
stable cortical representation of music is the outcome of sophisticated transformations of incoming
sound signals along the cortical hierarchy of auditory processing that generate a music
component in anterior regions of the superior temporal gyrus by means of top-down processes
that interact with acoustic features, guiding their processing.Musik ist unbestreitbarer Weise eine der definierenden Eigenschaften des Menschen. Dokumentiert
seit den fruÌhesten Tagen der Menschheit und in allen bekannten Kulturen vorhanden,
ist sie von allenMenschen nahezu gleichwahrnehmbar. Fasziniert von ihrerOmniprÀsenz
haben Wissenschaftler aller Disziplinen vor einigen hundert Jahren begonnen die mystische
Beziehung zwischen Musik und Mensch, sowie ihre enorme Bedeutung fuÌr selbigen zu untersuchen.
Seit einem vergleichsweise kurzem Zeitraum ist es durch den immensen Fortschritt
neurowissenschafticher Methoden auch möglich die kognitiven Prozesse, welche an der Verarbeitung
von Musik beteiligt, sind zu untersuchen. Innerhalb dieser Neurowissenschaft der
Musik hat sich ein GroĂteil der Forschungsarbeit darauf konzentriert wie Musik, als auditorischer
Stimulus, das menschliche Gehirn erreicht und wie sie initial verarbeitet wird, als auch
welche kolossallen Effekte sie auf selbiges hat und auch dadurch bewirken kann. Jedoch haben
die Zwischenschritte, also wie das menschliche Gehirn eintreffende Signale in eine scheinbar
spezialisierte und abstrakte ReprÀsentation vonMusik umwandelt, vergleichsweise wenig Aufmerksamkeit
erhalten. Um die dadurch entstandene LuÌcke zu adressieren, hat die hier vorliegende
Dissertation diese Prozesse und wie selbige durch Modelle erklÀrt werden können in
vier Projekten untersucht. Die ersten beiden Projekte beinhalteten die Herstellung und Implementierung
von zwei Toolboxen um erstens, inhÀrente Probleme der auditorischen Neurowissenschaft,
daher auch neurowissenschaftlicher Untersuchungen von Musik, zu verbessern
und zweitens, eine Basis fuÌr weitere Fortschritte durch Standardisierung und Automatisierung
zu schaffen. Im genaueren umfasste dies die stark beeintrÀchtigten Hörschwellen und
âfĂ€higkeiten in MRT-Untersuchungen und die erschwerte Lokalisation und Parzellierung des
menschlichen auditorischen Kortex als Kernstruktur auditiver Verarbeitung. Das dritte Projekt
befasste sich mit der augenscheinlichen Spezialisierung von Musik im menschlichen Gehirn
durch die Untersuchung funktionaler und organisatorischer Prinzipien des auditorischen
Kortex und Netzwerks bezuÌglich der Verarbeitung verschiedener auditorischer Kategorien vergleichbarer
sozialer Bedeutung, im genaueren ob die Wahrnehmung von Musik ein distinktes
und spezialisiertes neuronalenMuster hervorruft. Umeine ausfuÌhrliche Charakterisierung
der entsprechenden neuronalen Muster zu ermöglichen wurde die Segregation und Integration
der Regionen des auditorischen Kortex untersucht. Im vierten und letzten Projekt wurde
ein hochmultimodaler Ansatz,welcher fMRT, EEG, Verhalten undModelle verschiedener KomplexitÀt
beinhaltete, genutzt, umzu evaluieren, wie die zuvor genannten ReprÀsentationen von
Musik entlang der kortikalen Hierarchie der auditorischen Verarbeitung generiert und wie sie
möglicherweise durch Bottom-up- und Top-down-AnsÀtze beeinflusst werden. Die Ergebnisse
von Projekt 1 und 2 demonstrierten die Notwendigkeit fuÌr weitere Verbesserungen von MRTUntersuchungen
und die Definition eines Funktionsmodells des auditorischen Kortex, daHörxi
xii ZUSAMMENFASSUNG
schwellen und âfĂ€higkeiten stark in AbhĂ€ngigkeit der verwendeten Datenerwerbsprotokolle
variierten und die Lokalisation, sowie Parzellierung des menschlichen auditorischen Kortex
basierend auf den zugrundeliegenden AnsÀtzen drastisch divergiert. Projekt 3 zeigte, dass das
menschliche Gehirn tatsÀchlich eine spezialisierte ReprÀsentation vonMusik enthÀlt, da selbige
als einzige auditorische Kategorie ein bilaterales Netzwerk mit rechtshemisphÀrischer Gewichtung
evozierte. Aus diesemNetzwerk, welches die Rekrutierung anteriorer und posteriorer
Teile des auditorischen Kortex beinhaltete, resultierte eine scheinbar abstrakte ReprÀsentation
von Musik in anterioren Regionen des Gyrus temporalis superior, welche prÀferiert Musik enkodiert,
ungeachtet ob gesungen oder instrumental. Die Resultate von Projekt 4 deuten darauf
hin, dass der gesamte auditorische Kortex, erneut mit rechtshemisphÀrischer Gewichtung, an
der komplexen Verarbeitung vonMusik beteiligt ist, besonders aber anteriore Regionen, die bereits
genannten abstrakte ReprĂ€sentation hervorrufen, welche sich exzessiv uÌber die Zeitdauer
derWahrnehmung verÀndert und nicht hinreichend durch eines der getestetenModelle erklÀrt
werden kann. Die spezialisierten und abstrakten Eigenschaften dieser ReprÀsentationen wurden
weiterhin durch die prÀdiktiven FÀhigkeiten der getestetenModelle unterstrichen, daModelle,
welche entweder auf höheren Eigenschaften wie VerhaltensreprÀsentationen und mentalen
Konzepten oder komplexen akustischen Eigenschaften basierten, stets Modelle, welche
auf niederen Attributen wie simplen akustischen Eigenschaften basierten, uÌbertrafen. ZusĂ€tzlich
konnte kein Effekt von Faktoren, wie z.B. musikalisches Training, welche bekanntermaĂen
auditorische und daherMusikverarbeitung beeinflussen, nachgewiesen werden.
Zusammengefasst deuten die Ergebnisse der Projekte darauf, hin dass die spezialisierte und
stabile kortikale ReprÀsentation vonMusik ein Resultat komplexer Prozesse ist, welche eintreffende
Signale entlang der kortikalen Hierarchie auditorischer Verarbeitung in eine abstrakte
ReprÀsentation vonMusik innerhalb anteriorer Regionen des Gyrus temporalis superior durch
Top-Down-Prozesse, welche mit akustischen Eigenschaften interagieren und deren Verarbeitung
steuern, umwandeln
Recommended from our members
Fiscal Year 1995
The mission of the Engineering Research, Development, and Technology Program at Lawrence Livermore National Laboratory (LLNL) is to develop the knowledge base, process technologies, specialized equipment, tools and facilities to support current and future LLNL programs. Engineering`s efforts are guided by a strategy that results in dual benefit: first, in support of Department of Energy missions, such as national security through nuclear deterrence; and second, in enhancing the nation`s economic competitiveness through their collaboration with US industry in pursuit of the most cost-effective engineering solutions to LLNL programs. To accomplish this mission, the Engineering Research, Development, and Technology Program has two important goals: (1) identify key technologies relevant to LLNL programs where they can establish unique competencies, and (2) conduct high-quality research and development to enhance their capabilities and establish themselves as the world leaders in these technologies. To focus Engineering`s efforts, technology thrust areas are identified and technical leaders are selected for each area. The thrust areas are comprised of integrated engineering activities, staffed by personnel from the nine electronics and mechanical engineering divisions, and from other LLNL organizations. This annual report, organized by thrust area, describes Engineering`s activities for fiscal year 1995. The report provides timely summaries of objectives methods, and key results from eight thrust areas: computational electronics and electromagnetics; computational mechanics; microtechnology; manufacturing technology; materials science and engineering; power conversion technologies; nondestructive evaluation; and information engineering
GSI Scientific Report 2016
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