A statistical model for combustion resonance from a DI diesel engine with applications

Introduced in this paper is a Bayesian model for isolating the resonant frequency from combustion chamber resonance. The model shown in this paper focused on characterising the initial rise in the resonant frequency to investigate the rise of in-cylinder bulk temperature associated with combustion. By resolving the model parameters, it is possible to determine: the start of pre-mixed combustion, the start of diffusion combustion, the initial resonant frequency, the resonant frequency as a function of crank angle, the in-cylinder bulk temperature as a function of crank angle and the trapped mass as a function of crank angle. The Bayesian method allows for individual cycles to be examined without cycle-averaging|allowing inter-cycle variability studies. Results are shown for a turbo-charged, common-rail compression ignition engine run at 2000 rpm and full load.

Studies on the mechanism of the peroxyoxalate chemiluminescence reaction Part 1. Confirmation of 1,2-dioxetanedione as an intermediate using 13C nuclear magnetic resonance spectroscopy

A simple model peroxyoxalate chemiluminescence system was monitored directly across a range of temperatures (from −80 to +20 °C) using ¹³C nuclear magnetic resonance spectroscopy. These experiments were made possible by the utilisation of ¹³C doubly labelled oxalyl chloride, which was reacted with anhydrous hydrogen peroxide in dry tetrahydrofuran. Ab initio quantum calculations were also performed to estimate the ¹³C nuclear magnetic resonance (NMR) shift of the most commonly postulated key intermediate 1,2-dioxetanedione and this data, in concert with the spectroscopic evidence, confirmed its presence during the reaction.

Studies on the mechanism of the peroxyoxalate chemiluminescence reaction : part 2, further identification of intermediates using 2D EXSY 13C nuclear magnetic resonance spectroscopy

Further consideration has been given to the reaction pathway of a model peroxyoxalate chemiluminescence system. Again utilising doubly labelled oxalyl chloride and anhydrous hydrogen peroxide, 2D EXSY ¹³C nuclear magnetic resonance (NMR) spectroscopy experiments allowed for the characterisation of unknown products and key intermediate species on the dark side of the peroxyoxalate chemiluminescence reaction. Exchange spectroscopy afforded elucidation of a scheme comprised of two distinct mechanistic pathways, one of which contributes to chemiluminescence. ¹³C NMR experiments carried out at varied reagent molar ratios demonstrated that excess amounts of hydrogen peroxide favoured formation of 1,2-dioxetanedione: the intermediate that, upon thermolysis, has been long thought to interact with a fluorophore to produce light.

A microscopic competition model and its dynamics analysis on network attacks

Modeling network traffic has been a critical task in the development of Internet. Attacks and defense are prevalent in the current Internet. Traditional network models such as Poisson-related models do not consider the competition behaviors between the attack and defense parties. In this paper, we present a microscopic competition model to analyze the dynamics among the nodes, benign or malicious, connected to a router, which compete for the bandwidth. The dynamics analysis demonstrates that the model can well describe the competition behavior among normal users and attackers. Based on this model, an anomaly attack detection method is presented. The method is based on the adaptive resonance theory, which is used to learn the model by normal traffic data. The evaluation shows that it can effectively detect the network attacks.

A full body musculoskeletal model based on flexible multibody simulation approach utilised in bone strain analysis during human locomotion

Load-induced strains applied to bone can stimulate its development and adaptation. In order to quantify the incident strains within the skeleton, in vivo implementation of strain gauges on the surfaces of bone is typically used. However, in vivo strain measurements require invasive methodology that is challenging and limited to certain regions of superficial bones only such as the anterior surface of the tibia. Based on our previous study [Al Nazer et al. (2008) J Biomech. 41:1036–1043], an alternative numerical approach to analyse in vivo strains based on the flexible multibody simulation approach was proposed. The purpose of this study was to extend the idea of using the flexible multibody approach in the analysis of bone strains during physical activity through integrating the magnetic resonance imaging (MRI) technique within the framework. In order to investigate the reliability and validity of the proposed approach, a three-dimensional full body musculoskeletal model with a flexible tibia was used as a demonstration example. The model was used in a forward dynamics simulation in order to predict the tibial strains during walking on a level exercise. The flexible tibial model was developed using the actual geometry of human tibia, which was obtained from three-dimensional reconstruction of MRI. Motion capture data obtained from walking at constant velocity were used to drive the model during the inverse dynamics simulation in order to teach the muscles to reproduce the motion in the forward dynamics simulation. Based on the agreement between the literature-based in vivo strain measurements and the simulated strain results, it can be concluded that the flexible multibody approach enables reasonable predictions of bone strain in response to dynamic loading. The information obtained from the present approach can be useful in clinical applications including devising exercises to prevent bone fragility or to accelerate fracture healing.

A hybrid neural network model for noisy data regression

A hybrid neural network model, based on the fusion of fuzzy adaptive resonance theory (FA ART) and the general regression neural network (GRNN), is proposed in this paper. Both FA and the GRNN are incremental learning systems and are very fast in network training. The proposed hybrid model, denoted as GRNNFA, is able to retain these advantages and, at the same time, to reduce the computational requirements in calculating and storing information of the kernels. A clustering version of the GRNN is designed with data compression by FA for noise removal. An adaptive gradient-based kernel width optimization algorithm has also been devised. Convergence of the gradient descent algorithm can be accelerated by the geometric incremental growth of the updating factor. A series of experiments with four benchmark datasets have been conducted to assess and compare effectiveness of GRNNFA with other approaches. The GRNNFA model is also employed in a novel application task for predicting the evacuation time of patrons at typical karaoke centers in Hong Kong in the event of fire. The results positively demonstrate the applicability of GRNNFA in noisy data regression problems.

Improved GART neural network model for pattern classification and rule extraction with application to power systems

Generalized adaptive resonance theory (GART) is a neural network model that is capable of online learning and is effective in tackling pattern classification tasks. In this paper, we propose an improved GART model (IGART), and demonstrate its applicability to power systems. IGART enhances the dynamics of GART in several aspects, which include the use of the Laplacian likelihood function, a new vigilance function, a new match-tracking mechanism, an ordering algorithm for determining the sequence of training data, and a rule extraction capability to elicit if-then rules from the network. To assess the effectiveness of IGART and to compare its performances with those from other methods, three datasets that are related to power systems are employed. The experimental results demonstrate the usefulness of IGART with the rule extraction capability in undertaking classification problems in power systems engineering.

Parameter estimation of the BOLD fMRI model within a general particle filter framework

This work demonstrates a novel Bayesian learning approach for model based analysis of Functional Magnetic Resonance (fMRI) data. We use a physiologically inspired hemodynamic model and investigate a method to simultaneously infer the neural activity together with hidden state and the physiological parameter of the model. This joint estimation problem is still an open topic. In our work we use a Particle Filter accompanied with a kernel smoothing approach to address this problem within a general filtering framework. Simulation results show that the proposed method is a consistent approach and has a good potential to be enhanced for further fMRI data analysis.

An examination of the influence of visuomotor associations on interpersonal motor resonance

The adaptation account of mirror neurons in humans proposes that mirror systems have been selected for in evolution to facilitate social cognition. By contrast, a recent "association" account of mirror neurons in humans argues that mirror systems are not the result of a specific adaptation, but of sensorimotor learning arising from concurrent visual and motor activity. Here, we used transcranial magnetic stimulation (TMS) and electromyography (EMG) to evaluate whether visuomotor associations affect interpersonal motor resonance, a putative measure of mirror system activity. 18 participants underwent two TMS sessions exploring whether visuomotor associations established throughout one׳s lifespan, namely common movements and movements generated from one׳s own perspective, are associated with increased putative mirror system activity. Our results showed no overall difference in interpersonal motor resonance to common versus uncommon actions, or actions presented from an egocentric (self) versus an allocentric (other) perspective. We did, however, observe increased interpersonal motor resonance within the abductor digiti minimi (ADM) muscle in response to allocentric compared to egocentric movements. As the association model predicts stronger mirror system response to actions with stronger visuomotor associations, such as common movements and those presented from an egocentric perspective, our findings provide little evidence to support the association model.

A clustering-based failure mode and effect analysis model and its application to the edible bird nest industry

Failure mode and effect analysis (FMEA) is a popular safety and reliability analysis tool in examining potential failures of products, process, designs, or services, in a wide range of industries. While FMEA is a popular tool, the limitations of the traditional Risk Priority Number (RPN) model in FMEA have been highlighted in the literature. Even though many alternatives to the traditional RPN model have been proposed, there are not many investigations on the use of clustering techniques in FMEA. The main aim of this paper was to examine the use of a new Euclidean distance-based similarity measure and an incremental-learning clustering model, i.e., fuzzy adaptive resonance theory neural network, for similarity analysis and clustering of failure modes in FMEA; therefore, allowing the failure modes to be analyzed, visualized, and clustered. In this paper, the concept of a risk interval encompassing a group of failure modes is investigated. Besides that, a new approach to analyze risk ordering of different failure groups is introduced. These proposed methods are evaluated using a case study related to the edible bird nest industry in Sarawak, Malaysia. In short, the contributions of this paper are threefold: (1) a new Euclidean distance-based similarity measure, (2) a new risk interval measure for a group of failure modes, and (3) a new analysis of risk ordering of different failure groups. © 2014 The Natural Computing Applications Forum.

Molecular architecture of Aβ fibrils grown in cerebrospinal fluid solution and in a cell culture model of Aβ plaque formation

OBJECTIVES: The detailed structure of brain-derived Aβ amyloid fibrils is unknown. To approach this issue, we investigate the molecular architecture of Aβ(1-40) fibrils grown in either human cerebrospinal fluid solution, in chemically simple phosphate buffer in vitro or extracted from a cell culture model of Aβ amyloid plaque formation. METHODS: We have used hydrogen-deuterium exchange (HX) combined with nuclear magnetic resonance, transmission electron microscopy, seeding experiments both in vitro and in cell culture as well as several other spectroscopic measurements to compare the morphology and residue-specific conformation of these different Aβ fibrils. RESULTS AND CONCLUSIONS: Our data reveal that, despite considerable variations in morphology, the spectroscopic properties and the pattern of slowly exchanging backbone amides are closely similar in the fibrils investigated. This finding implies that a fundamentally conserved molecular architecture of Aβ peptide fold is common to Aβ fibrils.