1,721,156 research outputs found
Impact of Fuzz Button Degradation on AM and PRBS Signal Transmission
Full text linkAs elastic electrical connectors, fuzz buttons provide a vertical and solderless electrical interconnection in microwave modules to enhance the integration. However, prolonged use in harsh environments poses a risk of potential failure in electronic components, potentially compromising communication system reliability. This work studies the impact of fuzz button degradation in harsh environments on analog modulation (AM) and pseudo random binary sequence (PRBS) signal transmission using theoretical analysis and experimental testing. Accelerated tests are designed to obtain the fuzz button samples with different degradation levels. The surface morphology observation and elemental analysis are conducted to analyse the degradation mechanism. In addition, a transmission channel with fuzz button interconnections is designed and the corresponding equivalent circuit model is developed. Based on the proposed circuit model, the effects of fuzz button degradation on the integrity of both AM signal and PRBS signal are investigated by analysing the metrics such as waveform, eye diagram and bit error rate (BER) of the output signal. In addition, the effects of the carrier frequency of AM signals, and the transmission rate of the PRBS signals on signal transmission are also investigated. The simulation results of the circuit model show good agreements with experimental tests. The research results provide a better understanding regarding the potentially corrosive effects of harsh environments on fuzz button connectors and the negative effects on the signal integrity. Moreover, the research results provide comprehensive data support for identifying key features that are used for the development of machine learning models for fault diagnosis and localisation in radio frequency (RF) circuits with fuzz button interconnections
Novel Data Mining Approaches for Detecting Quantitative Trait Loci of Bone Mineral Density in Genome-Wide Linkage Analysis
No full textHaseman-Elston (H-E) regression is a commonly used conventional approach for detecting quantitative trait loci (QTLs), which regulate the quantitative phenotype based on the Identical-By-Descent (IBD) information between twins in Genome-wide scan. However, this approach only considers genetic effect at individual loci, but not any interaction between genes. A Pair-Wise H-E regression (PWH-E) and a Feature Screening Approach (FSA) are proposed in this paper to take gene-gene interaction into account when detecting QTLs. After testing these approaches with several series of simulation studies, they are applied to a real-world bone mineral density (BMD) dataset, and find three site specific sets of potential QTLs. Further comparison analyses show that our results not only corroborate the 14 findings from previous published studies, but also suggest 22 new QTLs of BMD
Adaptive K-Means for Clustering Air Mass Trajectories
No full textClustering air mass trajectories is used to identify source regions of certain chemical species. Current clustering methods only use the trajectory coordinates as clustering variables, and as such, are unable to differentiate between similar shaped trajectories that have different source regions and/or seasonal differences. This can lead to a higher variance in the chemical composition within each cluster and loss of information. We propose an adaptive K-means clustering algorithm that uses both the trajectory variables and the associated chemical value. We show, using carbon monoxide data from the Cape Verde for 2007, that our method produces a far more informative clustering than the existing standard method, whilst achieving a lower level of subjectivity
A Novel Ensemble of Distance Measures for Feature Evaluation: Application to Sonar Imagery
No full textMapping interesting regions in qualitative sidescan sonar imagery predominantly relies on an expensive human interpretation process. It would therefore be useful to automate components of this task with a feature-based, Machine Learning system. We must first establish a framework for reliably and efficiently evaluating the features. A novel ensemble of probabilistic distance measures is proposed, as an objective function for this purpose. The idea is motivated by the fact that different distance measures yield conflicting feature ranking results. In the ensemble, distances can be combined to produce a consensus rank score. As a test case, we find a sub-optimal parameterisation of a Co-occurrence Matrix, for identifying textures peculiar to the tube-building worm, Sabellaria spinulosa. A strong correlation is found between ensemble scores and classification accuracies. The proposed methodology is applicable to any sonar imagery, classification task or feature groups
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Knowledge Distillation for Generative Models
No full textThis thesis investigates and introduces novel Knowledge Distillation techniques tailored for deep generative models. State-of-the-art generative models in domains such as image generation and text generation (Large Language Models) achieve impressive performance, but their substantial size and computational requirements pose significant challenges for practical deployment, particularly in resource-constrained environments. Knowledge Distillation, a model compression technique that transfers knowledge from a large teacher model to a compact student model, offers a potential solution. However, traditional KD methods, primarily designed for discriminative tasks, face unique challenges when applied to generative tasks, such as capturing complex distributional properties, ensuring global consistency, and effectively leveraging the teacher’s “dark knowledge” from continuous outputs rather than simply probabilities. To address these challenges, this thesis introduces several KD strategies. For Image Super-Resolution models, we propose Data Augmentations empowered KD (AugKD), which leverages auxiliary distillation samples generated through data augmentations and incorporates label consistency regularization to more effectively utilize the teacher’s distributional information. Additionally, we introduce Multi-granularity Mixture of Priors KD (MiPKD), which transfers teacher knowledge at multiple granularities using feature and block prior mixers, exhibiting consistent superiority across different compression settings and model architectures. For Large Language Model distillation, we propose a Multi-Granularity Semantic Revision framework, including a sequence-level correction and re-generation (SCRG) strategy, a token-level Distribution-Adaptive Clipping Kullback-Leibler (DAC-KL) loss function, and span-level correlation consistency. Experimental results on multiple LLM pairs validate the effectiveness of these methods, showing significant performance improvements for student models compared to existing approaches.</p
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