Browsing by Author "Yousuf MU"

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    Anchoring Mechanism for Capsule Endoscope: Mechanical Design, Fabrication and Experimental Evaluation.
    (MDPI (Basel, Switzerland), 2022-11-22) Rehan M; Yeo AG; Yousuf MU; Avci E; Li Y
    Capsule endoscopes are widely used to diagnose gut-related problems, but they are passive in nature and cannot actively move inside the gut. This paper details the design process and development of an anchoring mechanism and actuation system to hold a capsule in place within the small intestine. The design centres around the mechanical structure of the anchor that makes use of compliant Sarrus linkage legs, which extend to make contact with the intestine, holding the capsule in place. Three variants with 2 legs, 3 legs and 4 legs of the anchoring mechanism were tested using a shape memory alloy spring actuator (5 mm × ϕ 3.4 mm). The experiments determine that all the variants can anchor at the target site and resist peristaltic forces of 346 mN. The proposed design is well suited for an intestine with a diameter of 19 mm. The proposed design allows the capsule endoscopes to anchor at the target site for a better and more thorough examination of the targeted region. The proposed anchoring mechanism has the potential to become a vital apparatus for clinicians to use with capsule endoscopes in the future.
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    Short-Term Wind Speed Forecasting Based on Hybrid MODWT-ARIMA-Markov Model
    (IEEE, 2021-06-08) Yousuf MU; Al-Bahadly I; Avci E; Do TD
    Markov chains (MC) are statistical models used to predict very short to short-term wind speed accurately. Such models are generally trained with a single moving window. However, wind speed time series do not possess an equal length of behavior for all horizons. Therefore, a single moving window can provide reasonable estimates but is not an optimal choice. In this study, a forecasting model is proposed that integrates MCs with an adjusting dynamic moving window. The model selects the optimal size of the window based on a similar approach to the leave-one-out method. The traditional model is further optimized by introducing a self-adaptive state categorization algorithm. Instead of synthetically generating time series, the modified model directly predicts one-step ahead wind speed. Initial results indicate that adjusting the moving window MC prediction model improved the forecasting performance of a single moving window approach by 50%. Based on preliminary findings, a novel hybrid model is proposed integrating maximal overlap discrete wavelet transform (MODWT) with auto-regressive integrated moving average (ARIMA) and adjusting moving window MC. It is evident from the literature that MC models are suitable for predicting residual sequences. However, MCs were not considered as a primary forecasting model for the decomposition-based hybrid approach in any wind forecasting studies. The improvement of the novel model is, on average, 55% for single deep learning models and 30% for decomposition-based hybrid models.
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    Wind speed prediction for small sample dataset using hybrid first-order accumulated generating operation-based double exponential smoothing model
    (John Wiley & Sons, Inc, 2022-03-09) Yousuf MU; Al-Bahadly I; Avci E
    Wind power generation has recently emerged in many countries. Therefore, the availability of long-term historical wind speed data at various potential wind farm sites is limited. In these situations, such forecasting models are needed that comprehensively address the uncertainty of raw data based on small sample size. In this study, a hybrid first-order accumulated generating operation-based double exponential smoothing (AGO-HDES) model is proposed for very short-term wind speed forecasts. Firstly, the problems of traditional Holt's double exponential smoothing model are highlighted considering the wind speed data of Palmerston North, New Zealand. Next, three improvements are suggested for the traditional model with a rolling window of six data points. A mixed initialization method is introduced to improve the model performance. Finally, the superiority of the novel model is discussed by comparing the accuracy of the AGO-HDES model with other forecasting models. Results show that the AGO-HDES model increased the performance of the traditional model by 10%. Also, the modified model performed 7% better than other considered models with three times faster computational time.