BURA Community:
http://bura.brunel.ac.uk/handle/2438/8630
2024-03-15T09:42:16Z
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A novel framework for motor bearing fault diagnosis based on multi-transformation domain and multi-source data
http://bura.brunel.ac.uk/handle/2438/28538
Title: A novel framework for motor bearing fault diagnosis based on multi-transformation domain and multi-source data
Authors: Xue, Y; Wen, C; Wang, Z; Liu, W; Chen, G
Abstract: Through the application of deep learning and multi-sensor data, fault features can be automatically extracted and valuable information can be integrated to tackle intricate challenges in motor bearing fault diagnosis. Most existing fusion models focus primarily on the original time series signal with information extraction largely restricted to the time domain (without extensions into multiple transformation domains). Also, in most fusion models, the sensor fusion level is kept relatively simple which could lead to the oversight of correlations and complementarities among the information. To enhance the recognition capability of diagnostic network features, in this paper, we propose a novel framework for motor bearing fault diagnosis from the perspectives of multi-transformation domain and multi-source data fusion. Within this framework, feature extraction and fusion from various source data are achieved in the time domain, frequency domain, and time–frequency domain. Distinct independent networks are set up within these domains: one network is designated for overseeing feature fusion, while the others are dedicated to extracting features from individual sensors. To support the extraction of pivotal features across multiple fusion layers in various transformation domains, several fusion nodes are inserted between the layers of the multiple feature extraction networks and the feature summarization network. Furthermore, a channel attention mechanism is introduced as a fusion strategy that serves to pinpoint the significance of different features, thus enhancing the efficiency of feature extraction. Experimental evaluation reveals the efficacy of the proposed model and highlights its noteworthy performance attributes such as scalability and universality.
Description: © Elsevier Ltd. All rights reserved. This manuscript version is under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/ | https://www.elsevier.com/about/policies/sharing
2024-01-11T00:00:00Z
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A Preliminary Analysis of Software Metrics in Decentralised Applications
http://bura.brunel.ac.uk/handle/2438/28468
Title: A Preliminary Analysis of Software Metrics in Decentralised Applications
Authors: Ibba, G; Khullar, S; Tesfai, E; Neykova, R; Aufiero, S; Ortu, M; Bartolucci, S; Destefanis, G
Abstract: This study examines software metrics in decentralized applications (dApps) to analyze their structural and behavioral characteristics as they grow in complexity. Sixty dApps were categorized into Small (3 to 29 contracts), Medium (30 to 46 contracts), and Large (47 to 206 contracts) based on their contract count. Initial analysis showed a non-normal data distribution, leading to the use of Spearman's correlation method. Findings revealed that Medium dApps have strong correlations between metrics like 'Average Local Variables' and 'Maximum Local Variables', while Large dApps show higher correlations between 'Number of Functions' and 'State Variable Count', indicating more complex contract structures. The higher Coupling Between Objects (CBO) in large dApps suggests increased interactions with other contracts or libraries, potentially elevating security risks. These insights are valuable for developers and stakeholders in the blockchain and IoT sectors, aiding in understanding how dApps evolve with increasing complexity and the implications on software metric relationships.
2023-11-12T00:00:00Z
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MindTheDApp: A Toolchain for Complex Network-Driven Structural Analysis of Ethereum-based Decentralised Applications
http://bura.brunel.ac.uk/handle/2438/28467
Title: MindTheDApp: A Toolchain for Complex Network-Driven Structural Analysis of Ethereum-based Decentralised Applications
Authors: Ibba, G; Aufiero, S; Bartolucci, S; Neykova, R; Ortu, M; Tonelli, R; Destefanis, G
Abstract: This paper presents MindTheDApp, a toolchain designed specifically for the structural analysis of Ethereum-based Decentralized Applications (DApps), with a distinct focus on a complex network-driven approach. Unlike existing tools, our toolchain combines the power of ANTLR4 and Abstract Syntax Tree (AST) traversal techniques to transform the architecture and interactions within smart contracts into a specialized bipartite graph. This enables advanced network analytics to highlight operational efficiencies within the DApp’s architecture. The bipartite graph generated by the proposed tool comprises two sets of nodes: one representing smart contracts, interfaces, and libraries, and the other including functions, events, and modifiers. Edges in the graph connect functions to smart contracts they interact with, offering a granular view of interdependencies and execution flow within the DApp. This network-centric approach allows researchers and practitioners to apply complex network theory in understanding the robustness, adaptability, and intricacies of decentralized systems. Our work contributes to the enhancement of security in smart contracts by allowing the visualisation of the network, and it provides a deep understanding of the architecture and operational logic within DApps. Given the growing importance of smart contracts in the blockchain ecosystem and the emerging application of complex network theory in technology, our toolchain offers a timely contribution to both academic research and practical applications in the field of blockchain technology.
2024-02-13T00:00:00Z
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Sub-Band Cascaded CSP-based Deep Transfer Learning for Cross-Subject Lower Limb Motor Imagery Classification
http://bura.brunel.ac.uk/handle/2438/28410
Title: Sub-Band Cascaded CSP-based Deep Transfer Learning for Cross-Subject Lower Limb Motor Imagery Classification
Authors: Wei, M; Yang, R; Huang, M; Ni, J; Wang, Z; Liu, X
Abstract: Lower limb motor imagery (MI) classification is a challenging research topic in brain-computer interface (BCI) due to excessively close physiological representation of left and right lower limb movements in the human brain. Moreover, MI signals have severely subject-specific characteristics. The classification schemes designed for a specific subject in previous studies could not meet the requirements of cross-subject classification in a generic BCI system. Therefore, this study aimed to establish a cross-subject lower limb MI classification scheme. Three novel sub-band cascaded common spatial pattern (SBCCSP) algorithms were proposed to extract representative features with low redundancy. The validations had been conducted based on the lower limb stepping-based MI signals collected from subjects performing MI tasks in experiments. The proposed schemes with three SBCCSP algorithms have been validated with better accuracy and running time performances than other common spatial pattern (CSP) variants with the best average accuracy of 98.78%. This study provides the first investigation of a cross-subject MI classification scheme based on experimental stepping-based MI signals. The proposed scheme will make an essential contribution to developing generic BCI systems for lower limb auxiliary and rehabilitation applications.
2023-12-04T00:00:00Z