An optimal factor analysis approach to improve the wavelet-based image resolution enhancement techniques

The existing wavelet-based image resolution enhancement techniques have many assumptions, such as limitation of the way to generate low-resolution images and the selection of wavelet functions, which limits their applications in different fields. This paper initially identifies the factors that effectively affect the performance of these techniques and quantitatively evaluates the impact of the existing assumptions. An approach called Optimal Factor Analysis employing the genetic algorithm is then introduced to increase the applicability and fidelity of the existing methods. Moreover, a new Figure of Merit is proposed to assist the selection of parameters and better measure the overall performance. The experimental results show that the proposed approach improves the performance of the selected image resolution enhancement methods and has potential to be extended to other methods

Reflection and perspective on international workplace learning research: A literature review

In recent years, researchers\u27 interest in workplace learning has increased dramatically. Many journals show the current research status of workplace learning. This study uses bibliometrics, cluster analysis, knowledge map to analyze 1764 literatures in 8 journals of workplace learning in 2010-2019. Research findings: (1) The research topics of workplace learning are relatively micro and diverse. Workplace learning theory, influencing factors of workplace learning, workplace learning evaluation, leadership, performance improvement and organizational change are the current research hotspot. (2) Empirical research is the majority research method. (3) Workplace learning theoretical model, informal learning, performance improvement, organizational learning, innovation, leadership are the future research trends

MOLECULAR EVOLUTION OF EBOLA: A BIOINFORMATICS APPROACH

In this project, the proteomes of Ebola and its deadly relatives from the Filoviridae (Filovirus) family are studied to better understand the evolution of these viruses that may help in developing better antiviral drugs and vaccines. Specifically, we construct three-dimensional models for the seven proteins in the proteome using the homology modeling approach. Then, we find the conserved and diverse regions in each protein across species and annotate them with the protein binding sites involved in virus-virus interactions and in human-virus interactions. We conclude that highly conserved regions are linked to the areas involved in virus-virus interactions, while the highly diverse regions are linked to those involved in human-virus interactions

Comparison of alternatives to amplitude thresholding for onset detection of acoustic emission signals

Acoustic Emission (AE) monitoring can be used to detect the presence of damage as well as determine its location in Structural Health Monitoring (SHM) applications. Information on the time difference of the signal generated by the damage event arriving at different sensors in an array is essential in performing localisation. Currently, this is determined using a fixed threshold which is particularly prone to errors when not set to optimal values. This paper presents three new methods for determining the onset of AE signals without the need for a predetermined threshold. The performance of the techniques is evaluated using AE signals generated during fatigue crack growth and compared to the established Akaike Information Criterion (AIC) and fixed threshold methods. It was found that the 1D location accuracy of the new methods was within the range of <1–7.1%<1–7.1% of the monitored region compared to 2.7% for the AIC method and a range of 1.8–9.4% for the conventional Fixed Threshold method at different threshold levels

Conditional Teacher-Student Learning

The teacher-student (T/S) learning has been shown to be effective for a variety of problems such as domain adaptation and model compression. One shortcoming of the T/S learning is that a teacher model, not always perfect, sporadically produces wrong guidance in form of posterior probabilities that misleads the student model towards a suboptimal performance. To overcome this problem, we propose a conditional T/S learning scheme, in which a "smart" student model selectively chooses to learn from either the teacher model or the ground truth labels conditioned on whether the teacher can correctly predict the ground truth. Unlike a naive linear combination of the two knowledge sources, the conditional learning is exclusively engaged with the teacher model when the teacher model's prediction is correct, and otherwise backs off to the ground truth. Thus, the student model is able to learn effectively from the teacher and even potentially surpass the teacher. We examine the proposed learning scheme on two tasks: domain adaptation on CHiME-3 dataset and speaker adaptation on Microsoft short message dictation dataset. The proposed method achieves 9.8% and 12.8% relative word error rate reductions, respectively, over T/S learning for environment adaptation and speaker-independent model for speaker adaptation.Comment: 5 pages, 1 figure, ICASSP 201