Search results for " Mach"
showing 10 items of 1388 documents
Nature Inspired Scenes for Guided Mindfulness Training: Presence, Perceived Restorativeness and Meditation Depth
2019
Practicing mindfulness-based stress reeducation and other contemplative practices generates a number of health and human performance benefits. However, limited access to qualified training and practice support, as well as poor practice environments, makes it difficult to sustain the habits necessary to develop the attentional regulation skills needed to benefit from mindfulness. In this paper, we report on our research, which focuses on developing immersive environments to support mindfulness-based stress reduction practices. We specifically look at how the design of a virtual environment can foster a restorative experience, if that restorative experience is associated with the depth of the…
OnMLM: An Online Formulation for the Minimal Learning Machine
2019
Minimal Learning Machine (MLM) is a nonlinear learning algorithm designed to work on both classification and regression tasks. In its original formulation, MLM builds a linear mapping between distance matrices in the input and output spaces using the Ordinary Least Squares (OLS) algorithm. Although the OLS algorithm is a very efficient choice, when it comes to applications in big data and streams of data, online learning is more scalable and thus applicable. In that regard, our objective of this work is to propose an online version of the MLM. The Online Minimal Learning Machine (OnMLM), a new MLM-based formulation capable of online and incremental learning. The achievements of OnMLM in our…
Extreme Minimal Learning Machine
2018
Extreme Learning Machine (ELM) and Minimal Learning Machine (MLM) are nonlinear and scalable machine learning techniques with randomly generated basis. Both techniques share a step where a matrix of weights for the linear combination of the basis is recovered. In MLM, the kernel in this step corresponds to distance calculations between the training data and a set of reference points, whereas in ELM transformation with a sigmoidal activation function is most commonly used. MLM then needs additional interpolation step to estimate the actual distance-regression based output. A natural combination of these two techniques is proposed here, i.e., to use a distance-based kernel characteristic in M…
Safety Assurance of a High Voltage Controller for an Industrial Robotic System
2020
Abstract Due to the risk of discharge sparks and ignition, there are strict rules concerning the safety of high voltage electrostatic systems used in industrial painting robots. In order to assure that the system fulfils its safety requirements, formal verification is an important tool to supplement traditional testing and quality assurance procedures. The work in this paper presents formal verification of the most important safety functions of a high voltage controller. The controller has been modelled as a finite state machine, which was formally verified using two different model checking software tools; Simulink Design Verifier and RoboTool. Five safety critical properties were specifie…
Verification of Well-Formed Communicating Recursive State Machines
2008
AbstractIn this paper we introduce a new (non-Turing equivalent) formal model of recursive concurrent programs called well-formed communicating recursive state machines (CRSM). CRSM extend recursive state machines (RSM) by allowing a restricted form of concurrency: a state of a module can be refined into a finite collection of modules (working in parallel) in a potentially recursive manner. Communication is only possible between the activations of modules invoked on the same fork. We study the model-checking problem of CRSM with respect to specifications expressed in a temporal logic that extends CaRet with a parallel operator (ConCaRet). We propose a decision algorithm that runs in time ex…
Verification of scope-dependent hierarchical state machines
2008
AbstractA hierarchical state machine (Hsm) is a finite state machine where a vertex can either expand to another hierarchical state machine (box) or be a basic vertex (node). Each node is labeled with atomic propositions. We study an extension of such model which allows atomic propositions to label also boxes (Shsm). We show that Shsms can be exponentially more succinct than Shsms and verification is in general harder by an exponential factor. We carefully establish the computational complexity of reachability, cycle detection, and model checking against general Ltl and Ctl specifications. We also discuss some natural and interesting restrictions of the considered problems for which we can …
Simulazione della propagazione di difetti a fatica mediante il modello di zona coesiva
2009
Le giunzioni incollate guadagnano sempre più mercato, nel campo delle costruzioni in genere, dove è necessario un alleggerimento delle strutture. Nel caso di geometrie di giunto semplici il dimensionamento avviene attraverso relazioni analitiche che restituiscono il valore massimo delle tensioni, il quale deve essere inferiore al limite di utilizzo dell’adesivo stesso. Quando le geometrie sono complesse l’approccio analitico diventa impraticabile, di conseguenza si provvede a verificare la correttezza della soluzione mediante analisi agli elementi finiti (EF). L’introduzione del modello di zona coesiva nell'analisi EF permette di simulare il danneggiamento ed il cedimento del giunto in cond…
Detecting global and local hippocampal shape changes in Alzheimer's disease using statistical shape models.
2012
Item does not contain fulltext The hippocampus is affected at an early stage in the development of Alzheimer's disease (AD). With the use of structural magnetic resonance (MR) imaging, we can investigate the effect of AD on the morphology of the hippocampus. The hippocampal shape variations among a population can be usually described using statistical shape models (SSMs). Conventional SSMs model the modes of variations among the population via principal component analysis (PCA). Although these modes are representative of variations within the training data, they are not necessarily discriminative on labeled data or relevant to the differences between the subpopulations. We use the shape des…
Computational Identification of Chemical Compounds with Potential Activity against Leishmania amazonensis using Nonlinear Machine Learning Techniques.
2019
Leishmaniasis is a poverty-related disease endemic in 98 countries worldwide, with morbidity and mortality increasing daily. All currently used first-line and second-line drugs for the treatment of leishmaniasis exhibit several drawbacks including toxicity, high costs and route of administration. Consequently, the development of new treatments for leishmaniasis is a priority in the field of neglected tropical diseases. The aim of this work is to develop computational models those allow the identification of new chemical compounds with potential anti-leishmanial activity. A data set of 116 organic chemicals, assayed against promastigotes of Leishmania amazonensis, is used to develop the the…
Autonomous artificial nanomotor powered by sunlight
2006
Light excitation powers the reversible shuttling movement of the ring component of a rotaxane between two stations located at a 1.3-nm distance on its dumbbell-shaped component. The photoinduced shuttling movement, which occurs in solution, is based on a “four-stroke” synchronized sequence of electronic and nuclear processes. At room temperature the deactivation time of the high-energy charge-transfer state obtained by light excitation is ≈10 μs, and the time period required for the ring-displacement process is on the order of 100 μs. The rotaxane behaves as an autonomous linear motor and operates with a quantum efficiency up to ≈12%. The investigated system is a unique example of an artif…