Microstructure evolution during fabrication and microstructure–property relationships in vapour-grown carbon nanofibre-reinforced aluminium matrix composites fabricated via powder metallurgy

Microstructure evolution of vapour-grown carbon nanofibre (VGCF)-reinforced aluminium matrix composites during fabrication and microstructure–property relationships of these materials were studied. Composites were fabricated using powder metallurgy, i.e. by mixing VGCFs and aluminium powder via ball-milling followed by sintering or hot extrusion. The mixing condition was selected to achieve small powder particle size and homogeneously dispersed VGCFs. Aluminium grains and VGCFs were elongated along the longitudinal direction of aluminium particles in the mixed powder. Detailed observation of the aluminium grains in the composites found grain size and morphology dominated by recrystallization. Apparently, grain growth was inhibited by VGCFs. Theoretical models considering strength increment due to grain refinement resulting from VGCF addition, load bearing of VGCFs, thermal mismatch of VGCFs and aluminium and Orowan effect were developed. Theoretical values coincided well with hardness, yield strength, and ultimate tensile strength of the composites, and thus the models could precisely explain the microstructure–property relationships.     

A study on the possibility of implementing a real-time stereoscopic 3D rendering TV system

3D video has recently seen a massive increase in exposure in our lives. However, differences between the viewing and shooting conditions for a film lead to disparities between the reformed media and the original three-dimensional effect, which cause severe visual fatigue to viewers and result in headaches and dizziness. In this paper, a series of image processing algorithms are introduced to overcome these problems. The image processing pipeline is composed of four steps, eye-pupil detection, stereo correspondence computation, saliency map generation, and 3D warping. Each step is implemented in an S3DS-3D rendering system and its time complexity is measured. From the results, it was found that real-time stereoscopic 3D rendering is impossible using only a software implementation because SIFT and optical flow calculation requires a significant amount of time. Therefore, these two algorithm blocks should be implemented with hardware acceleration. Fortunately, active research is being conducted on these issues and real-time processing is expected to become available soon for applications beyond full-HD TV screens. In addition, it was found that saliency map generation and 3D warping blocks also need to be implemented in hardware for full-HD display although they do not have significant time complexity compared to SIFT and optical flow algorithm blocks.     

Improved process for decontamination of whey by a continuous flow-through pulsed light system

The effect of a continuous flow-through pulsed light (PL) system on the inactivation of Listeria innocua was evaluated in different liquid substrates: distilled water, whey, diluted whey and skimmed whey. Reductions in L. innocua counts increased with number of pulses and total fluence. For similar total fluence, treatments consisting of a higher number of pulses but lower voltage were more effective in L. innocua inactivation due to a higher probability to expose cells to the incident light. Microbial inactivation by PL depended on the quantity of light transmitted in the range 230–290 nm by liquid substrates. Regardless of the limited effectiveness of PL for decontamination of whey, its shelf life during storage at 4 °C was extended by at least 7 days. In order to improve the antimicrobial effectiveness of PL, this treatment could be carried out at mild processing temperatures (60 °C), which was shown to exhibit a synergistic effect on the inactivation of L. innocua. This combined process appears as a promising technique to decontaminate complex substrates such as whey or to reduce the intensity of individual treatments to achieve the required level of microbial inactivation.     

In-plane light emission of organic light-emitting transistors with bilayer structure using ambipolar semiconducting polymers

The concept of using an ambipolar bilayer semiconducting heterostructure in organic light-emitting transistors (OLETs) is introduced to provide a new approach to achieve surface emission. The properties of top-gate-type bilayer OLETs with ambipolar materials based on two types of fluorene-type polymers used as an emissive layer and an electron blocking layer are investigated. Line-shaped yellow–green emission occurs near a hole-injection electrode. When hole transport is dominant in the upper layer which acts as an electron blocking layer, and electrons are injected into the lower layer, an in-plane light-emitting pattern is observed. The measured in-plane emission zone confirms that both hole and electron transport are determined to occur mainly along the different organic layers between the source and drain electrodes, and an in-plane recombination zone of electrons and holes exists near the bilayer organic interface. This work is anticipated to be useful for the development of in-plane light-emitting transistors.     

An effective approach to reinforced closed-cell Al-alloy foams with multiwalled carbon nanotubes

Exploring the reinforcing role of carbon nanotubes to obtain materials (polymers, metals, ceramics) with enhanced properties has been often attempted, but the success is strongly limited by the dispersing degree of carbon nanotubes. Here we report on an innovative colloidal approach to disperse the carbon nanotubes in the powders mixture of the precursor materials in order to profit from their reinforcing potential and obtain a new class of closed-cell metal foams. The feasibility of the proposed approach was demonstrated for aluminium foams reinforced with multi-walled carbon nanotubes. These nanocomposite metal foams synergistically combine the remarkable properties of both metal foams and carbon nanotubes. The results indicate that the tubular structure of carbon nanotubes is preserved throughout the entire the process. The carbon nanotubes are individually dispersed, stretched and randomly aligned in the aluminium-matrix of these closed-cell foams, thus potentiating their homogeneous 3D reinforcing role. Accordingly, the Vickers micro-hardness of the closed-cell foams was greatly enhanced.     

Review of pulsed thermal NDT: Physical principles,theory and data processing

This paper summarizes the basics of pulsed thermal nondestructive testing (TNDT) including theoretical solutions, data processing algorithms and practical implementation. Typical defects are discussed along with 1D analytical and multi-dimensional numerical solutions. Special emphasis is focused on defect characterization by the use of inverse solutions. A list of TNDT terms is provided. Applications of active TNDT, mainly in the aerospace industry, are discussed briefly, and some trends in the further development of this technique are described.     

Dynamic model of online information quality perceptions and impacts: a literature review

This paper reviews the literature related to the concept of information quality (IQ) in the context of the online environment to understand how it has been conceptualised in the Information Systems (IS) literature, to identify gaps in the literature, and to make recommendations for future research. This study also extends the Information Processing Model (IPM) and proposes a novel dynamic model of online IQ perceptions and impacts. To understand the online IQ construct, an in-depth review of the literature is conducted. Overall, we reviewed 508 journal articles published between 1995 and 2016. This study found that there is a substantial gap in the IS literature in regards to a full understanding of the online IQ construct and its relation with the IPM. More research is needed to understand the way individuals process and respond to online information. Further, although the importance of context in theory development has been emphasised in the literature, very few empirical articles have explored the impact of contextual factors on the IPM as well as perceived online IQ. The findings provide online vendors with important guidelines that should be considered to motivate individuals to acquire more information from their websites that could lead to more satisfied and loyal users.     

Model-Based Design Space Exploration for FPGA-based Image Processing Applications Employing Parameterizable Approximations

The increasing demand for low power consumption and high computational performance is outpacing available technological improvements in embedded systems. Approximate computing is a novel design paradigm trying to bridge this gap by leveraging the inherent error resilience of certain applications and trading in quality to achieve reductions in resource usage. Numerous approximation methods have emerged in this research field. While these methods are commonly demonstrated in isolation, their combination can increase the achieved benefits in complex systems. However, the propagation of errors throughout the system necessitates a global optimization of parameters, leading to an exponentially growing design space. Additionally, the parameterization of approximated components must consider potential cross-dependencies between them. This work proposes a systematic approach to integrate and optimally configure parameterizable approximate components in FPGA-based applications, focusing on low-level but high-bandwidth image processing pipelines. The design space is explored by a multi-objective genetic algorithm which takes parameter dependencies between different components into account. During the exploration, appropriate models are used to estimate the quality-resource trade-off for probed solutions without the need for time-consuming synthesis. We demonstrate and evaluate the effectiveness of our approach on two image processing applications that employ multiple approximations. The experimental results show that the proposed methods are able to produce a wide range of Pareto-optimal solutions, offering various choices regarding the desired quality-resource trade-off.     

Classification and regression models of audio and vibration signals for machine state monitoring in precision machining systems

We present a data-driven method for monitoring machine status in manufacturing processes. Audio and vibration data from precision machining are used for inference in two operating scenarios: (a) variable machine health states (anomaly detection); and (b) settings of machine operation (state estimation). Audio and vibration signals are first processed through Fast Fourier Transform and Principal Component Analysis to extract transformed and informative features. These features are then used in the training of classification and regression models for machine state monitoring. Specifically, three classifiers (K-nearest neighbors, convolutional neural networks and support vector machines) and two regressors (support vector regression and neural network regression) were explored, in terms of their accuracy in machine state prediction. It is shown that the audio and vibration signals are sufficiently rich in information about the machine that 100% state classification accuracy could be accomplished. Data fusion was also explored, showing overall superior accuracy of data-driven regression models.     

Bond graph modeling,design and experimental validation of a photovoltaic/fuel cell/ electrolyzer/battery hybrid power system

This work presents a complete bond graph modeling of a hybrid photovoltaic-fuel cell-electrolyzer-battery system. These are multi-physics models that will take into account the influence of temperature on the electrochemical parameters. A bond graph modeling of the electrical dynamics of each source will be introduced. The bond graph models were developed to highlight the multi-physics aspect describing the interaction between hydraulic, thermal, electrochemical, thermodynamic, and electrical fields. This will involve using the most generic modeling approach possible for managing the energy flows of the system while taking into account the viability of the system. Another point treated in this work is to propose. In this work, a new strategy for the power flow management of the studied system has been proposed. This strategy aims to improve the overall efficiency of the studied system by optimizing the decisions made when starting and stopping the fuel cell and the electrolyzer. It was verified that the simulation results of the proposed system, when compared to simulation results presented in the literature, that the hydrogen demand is increased by an average of 8%. The developed management algorithm allows reducing the fuel cell degradation by 87% and the electrolyzer degradation by 65%. As for the operating time of the electrolyzer, an increment of 65% was achieved, thus improving the quality of the produced hydrogen. The Fuel Cell's running time has been decreased by 59%. With the ambition to validate the models proposed and the associated commands, the development of this study gave rise to the creation of an experimental platform. Using this high-performance experimental platform, experimental tests were carried out and the results obtained are compared with those obtained by simulation under the same metrological conditions.