BURA Community:
http://bura.brunel.ac.uk/handle/2438/8621
2024-03-29T01:33:56ZSoftware component selection based on quality criteria using the analytic network process
http://bura.brunel.ac.uk/handle/2438/28600
Title: Software component selection based on quality criteria using the analytic network process
Authors: Nazir, S; Anwar, S; Khan, SA; Shahzad, S; Ali, M; Amin, R; Nawaz, M; Lazaridis, P; Cosmas, J
Abstract: Component based software development (CBSD) endeavors to deliver cost-effective and quality software systems through the selection and integration of commercially available software components. CBSD emphasizes the design and development of software systems using preexisting components. Software component reusability is an indispensable part of component based software development life cycle (CBSDLC), which consumes a significant amount of organization’s resources, that is, time and effort. It is convenient in component based software system (CBSS) to select the most suitable and appropriate software components that provide all the required functionalities. Selecting the most appropriate components is crucial for the success of the entire system. However, decisions regarding software component reusability are often made in an ad hoc manner, which ultimately results in schedule delay and lowers the entire quality system. In this paper, we have discussed the analytic network process (ANP) method for software component selection. The methodology is explained and assessed using a real life case study.2014-12-15T00:00:00ZRenewable energy sources integration via machine learning modelling: A systematic literature review
http://bura.brunel.ac.uk/handle/2438/28563
Title: Renewable energy sources integration via machine learning modelling: A systematic literature review
Authors: Alazemi, T; Darwish, M; Radi, M
Abstract: The use of renewable energy sources (RESs) at the distribution level has become increasingly appealing in terms of costs and technology, expecting a massive diffusion in the near future and placing several challenges to the power grid. Since RESs depend on stochastic energy sources —solar radiation, temperature and wind speed, among others— they introduce a high level of uncertainty to the grid, leading to power imbalance and deteriorating the network stability. In this scenario, managing and forecasting RES uncertainty is vital to successfully integrate them into the power grids. Traditionally, physical- and statistical-based models have been used to predict RES power outputs. Nevertheless, the former are computationally expensive since they rely on solving complex mathematical models of the atmospheric dynamics, whereas the latter usually consider linear models, preventing them from addressing challenging forecasting scenarios. In recent years, the advances in machine learning techniques, which can learn from historical data, allowing the analysis of large-scale datasets either under non-uniform characteristics or noisy data, have provided researchers with powerful data-driven tools that can outperform traditional methods. In this paper, a systematic literature review is conducted to identify the most widely used machine learning-based approaches to forecast RES power outputs. The results show that deep artificial neural networks, especially long-short term memory networks, which can accurately model the autoregressive nature of RES power output, and ensemble strategies, which allow successfully handling large amounts of highly fluctuating data, are the best suited ones. In addition, the most promising results of integrating the forecasted output into decision-making problems, such as unit commitment, to address economic, operational and managerial grid challenges are discussed, and solid directions for future research are provided.2024-02-14T00:00:00ZImproved Adversarial Transfer Network for Bearing Fault Diagnosis under Variable Working Conditions
http://bura.brunel.ac.uk/handle/2438/28550
Title: Improved Adversarial Transfer Network for Bearing Fault Diagnosis under Variable Working Conditions
Authors: Wang, J; Ahmed, H; Chen, X; Yan, R; Nandi, AK
Abstract: Bearings are one of the critical components of rotating machinery, and their failure can cause catastrophic consequences. In this regard, previous studies have proposed a variety of intelligent diagnosis methods. Most existing bearing fault diagnosis methods implicitly assume that the training and test sets are from the same distribution. However, in real scenarios, bearings have been working in complex and changeable working environments for a long time. The data during their working processes and the data used for model training cannot meet this condition. This paper proposes an improved adversarial transfer network for fault diagnosis under variable working conditions. Specifically, this paper combines an adversarial transfer network with a short-time Fourier transform to obtain satisfactory results with the lighter network. Then, this paper employs a channel attention module to enhance feature fusion. Moreover, this paper designs a novel domain discrepancy hybrid metric loss to improve model transfer learning performance. Finally, this paper verifies the method’s effectiveness on three datasets, including dual-rotor, a Case Western Reserve University dataset and the Ottawa dataset. The proposed method achieves average accuracy, surpassing other methods, and shows better domain alignment capabilities.
Description: Data Availability Statement:
Data are contained within the article.2024-03-01T00:00:00ZA Wideband Triple-Mode Differentially Fed Microstrip Patch Antenna
http://bura.brunel.ac.uk/handle/2438/28549
Title: A Wideband Triple-Mode Differentially Fed Microstrip Patch Antenna
Authors: Liu, X; Hu, W; Gao, S; Wen, L; Luo, Q; Xu, R; Liu, Y
Abstract: A wideband differentially-fed microstrip patch antenna (MPA) with tripe-resonant modes is presented in this letter. The proposed triple-mode MPA is realized by combining two dual-mode MPAs (MPA-I and MPA-II) with different resonant frequency ratios. Firstly, the TM0,1 mode and TM0,1/2 mode of dual-mode MPA-I can be concurrently excited by adding a pair of coupling shorted patches beside the strip MPA. The ratio of f0,1/2/f0,1 can be easily adjusted by moving the shorting pins between the strip MPA and shorted patches. Secondly, by properly designing the dimensions of a conventional MPA, the TM0,1 and TM2,1 modes of dual-mode MPA-II are simultaneously excited. To further reduce the ratio of f2,1/f0,1, four slots are elaborately etched on the conventional MPA. Finally, by combining the two dual-mode MPAs, a triple-mode MPA with the frequency ratio of f0,1/2:f2,1:f0,1 = 1.2:1.1:1 is realized. To verify the design concept, a prototype of triple-mode MPA was fabricated and measured. Experimental results show that the proposed microstrip antenna achieves a wide bandwidth of 26.5%, a low cross-polarization of -23 dB, and high harmonic suppression.2021-04-21T00:00:00Z