Faculty of Computing, Engineering and Media
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Browsing Faculty of Computing, Engineering and Media by Type "Conference"
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Item Open Access A 2d-Numerical Study on Slot Jet Applied to a Wind Turbine as a Circulation Control Technique(XII International Conference on Computational Heat, Mass and Momentum Transfer, 2019-09) Petracci, Ivano; Manni, Luca; Angelino, Matteo; Corasaniti, Sandra; Gori, FabioA study on the feasibility of the Circulation Control (CC) technique for wind turbines is proposed. The CC was born in aeronautic field to improve the lift force on the wings, allowing the short take-off and landing of aircraft. It consists in blowing air at a relatively high speed over a rounded trailing edge. The thin jet of air remains attached to the convex curved surface, imposing a certain curvature to the outer streamlines, and, hence, increasing the lift force of the airfoil. Aim of this study is to numerically investigate the advantages on a wind turbine, based on the S809 airfoil, taking into account the energy related considerations, as the cost of the jet production. The paper, after a thorough evaluation of the increase of the generated power, finds that this technique could be promising in the energy harvesting aim.Item Metadata only 2PROM: A two-phase image retrieval optimization on dataspace using predictive modeling(IEEE, 2012) Fanzou Tchuissang, G. N.; Wang, N.; Kuicheu, N. C.; Siewe, Francois; Xu, D.Item Open Access A 3D GBSM for high-speed train communication systems under deep cutting scenarios(IEEE, International Workshop on High Mobility Wireless Communications (HMWC), 2015, 2015-12) Feng, Liu; Fan, Pingzhi; Wang, Cheng-Xiang; Ghazal, AmmarThis paper proposes a novel three-dimensional (3D) cylinder geometry-based stochastic model (GBSM) for non-isotropic multiple-input multiple-output (MIMO) Rice fading channels in high-speed train (HST) wireless communications under deep cutting scenarios. Using a validated approximation, the closed-form expression of the space-time correlation function (ST CF) of the proposed GBSM is obtained. Different from two-dimensional (2D) channel models, in the 3D GBSM the elevation angles and the height of the base station (BS) antenna relative to the mobile station (MS) one are introduced. The numerical results show the rationality of the approximation and how the arrangements of antennas affect the ST CF.Item Embargo 3D Object Reconstruction with Deep Learning(Springer, 2024-05-06) Aremu, Stephen S.; Taherkhani, Aboozar; Liu, Chang; Yang, ShengxiangRecent advancements and breakthroughs in deep learning have accelerated the rapid development in the field of computer vision. Having recorded a huge success in 2D object perception and detection, a lot of progress has also been made in 3D object reconstruction. Since humans can infer and relate better with 3D world images by just a single view 2D image of the object, it is necessary to train computers to think in 3D to achieve some key applications of computer vision. The use of deep learning in 3D object reconstruction of single-view images is rapidly evolving and recording significant results. In this research, we explore the Facebook well-known hybrid approach called Mesh R-CNN that combines voxel generation and triangular mesh re-construction to generate 3D mesh structure of an object from a 2D single-view image. Although the reconstruction of objects with varying geometry and topology was achieved by Mesh R-CNN, the mesh quality was affected due to topological errors like self-intersection, causing non-smooth and rough mesh generation. In this research, Mesh R-CNN with Laplacian Smoothing (Mesh R-CNN-LS) was proposed to use the Laplacian smoothing and regularization algorithm to refine the non-smooth and rough mesh. The proposed Mesh R-CNN-LS helps to constrain the triangular deformation and generate a better and smoother 3D mesh. The proposed Mesh R-CNN-LS was compared with the original Mesh R-CNN on the Pix3D dataset and it showed better performance in terms of the loss and average precision score.Item Metadata only 3D Printing of Flexible Two Terminal Electronic Memory Devices(2017-12) Salaoru, Iulia; Maswoud, Salah; Paul, Shashi; Manjunatha, Krishna NamaRecent strategy in the electronics sector is to ascertain the ways to make cheap, flexible and environmentally friendly electronic devices. The 3D Inkjet printing technology is based on the Additive Manufacturing concept [1] and it is with no doubt capable of revolutionizing the whole system of manufacturing electronic devices including: material selection; design and fabrication steps and device configuration and architecture. 3D Inkjet printing technology (IJP) is one of the most promising technologies to reduce the harmful radiation/ heat generation and also achieve reduction in manufacturing cost. Here, we explore the potential of 3D – inkjet printing technology to provide an innovative approach for electronic devices in especially information storage elements by seeking to manufacture and characterize state-of-art fully inkjet printed two terminal electronic memory devices. In this work, an ink-jettable material was formulated, characterized and printed by a a piezoelectric Epson Sylus P50 Inkjet printing machine on a flexible substrate. The active printed layers were deposited into a functioning simple metal/insulator/metal structure. Firstly, from ink perspective, the main physical properties such as rheological behaviour; surface tension and wettability were investigated. Furthermore, an in-depth electrical characterization of the fabricated memory cells was carried out using HP4140B picoammeter and an HP4192A impedance analyser. [1] N.Hopkinson, R.Hague, P.Dickens, Rapid manufacturing; an industrial revolution for the digital age. West Sussex, UK, John Wiley and Sons; 2006 [2] Iulia Salaoru, Zuoxin Zhou, Peter Morris, Gregory Gibbons, Inkjet printing of polyvinyl alcohol multilayers for addiive manufacturing applications, J.Appl.Polym.Sci., 133(25), 43572 (2016) [3] Ruth Cherrington, B.M.Wood, Iulia Salaoru, Vannessa Goodship, Digital printing of titanium dioxide for dye sensitized solar cells, JoVE, e53963, (2016) [4] Iulia Salaoru, Zuoxin Zhou, Peter Morris, Gregory J. Gibbons, Inkjet-printed Polyvinyl Alcohol Multilayers, JoVE,123, e55093-e55093, (2017).Item Open Access 3D Simulation of Partial Discharge in High Voltage Power Networks(IWCS Inc, 2019-10-01) Ragusa, Antonella; Sasse, Hugh G.; Duffy, A. P.Partial discharge (PD) events arise inside power cables due to defects of cable’s insulation material, characterized by a lower electrical breakdown strength than the surrounding dielectric material. These electrical discharges cause signals to propagate along the cable, manifesting as noise phenomena. More significantly, they contribute to insulation degradation and can produce a disruptive effect with a consequent interruption of power network operation. PD events are, therefore, one of the best ‘early warning’ indicators of insulation degradation and, for this reason, the modeling and studying of such phenomena, together with the development of on-line PDs location methods, are important topics for network integrity assessment, and to define methods to improve the power networks’ Electricity Security. This paper presents a 3D model of PD events inside a void in epoxy-resin insulation cables for High Voltage (HV) power networks. The 3D model has been developed using the High Frequency (HF) Solver of CST Studio Suite® software. PD events of a few µs duration have been modelled and analyzed. The PD behavior has been investigated using varying electrical stress. A first study of the PD signal propagation in a power network is described.Item Metadata only 5 million lux meters(2007-05) Mardaljevic, John; Painter, B.; Andersen, M.Item Metadata only A Demand Response Framework to Overcome Network Overloading in Power Distribution Networks(Elsevier, 2021-04-14) Jibran, Muhammad; Nasir, Hasan Arshad; Qureshi, Faran; Ali, Usman; Jones, ColinThis paper considers the problem of network overloading in the power distribution networks of Pakistan, often resulting from the inability of the transmission system to transfer power from source to end-user during peak loads. This results in frequent power-outages and consumers at such times have to rely on alternative energy sources, e.g. Uninterrupted Power Supply (UPS) systems with batteries to meet their basic demand. In this paper, we propose a demand response framework to eliminate the problem of network overloading. The flexibility provided by the batteries at different houses connected to the same grid node is exploited by scheduling the flow of power from mains and batteries and altering the charging-discharging patterns of the batteries, thereby avoiding network overloading and any tripping of the grid node. This is achieved by casting the problem in an optimal control setting based on a prediction of power demand at a grid node and then solving it using a model predictive control strategy. We present a case study to demonstrate the application and efficacy of our proposed frameworkItem Open Access A dynamic preference-driven evolutionary algorithm for solving dynamic multi-objective problems(ACM, 2024-07-01) Wang, Xueqing; Zheng, Jinhua; Zou, Juan; Hou, Zhanglu; Liu, Yuan; Yang, ShengxiangConsidering the decision-maker's preference information in static multi-objective optimization problems (MOPs) has been extensively studied. However, incorporating dynamic preference information into dynamic MOPs is a relatively less explored area. This paper introduces a preference information-driven DMOEA and proposes a preference-based prediction method. Specifically, a preference-based inverse model is designed to respond to the time-varying preference information, and the model is used to predict an initial population for tracking the changing ROI. Furthermore, a hybrid prediction strategy, that combines a linear prediction model and estimation of population manifolds in the ROI, is proposed to ensure convergence and distribution of population when the preference remain constant. The experimental results show that the proposed algorithm has significant advantages over existing representative DMOEAs through experimental tests on 19 common test problems.Item Metadata only A Multispectral Image Classification Framework for Estimating the Operational Risk of Lethal Wilt in Oil Palm Crops(Springer Nature, 2023-04-09) Pena, Alejandro; Puerta, Alejandro; Bonet, Isis; Caraffini, Fabio; Ochoa, Ivan; Gongora, Mario AugustoOperational risk is the risk associated with business operations in an organisation. With respect to agricultural crops, in particular, operational risk is a fundamental concept to establish differentiated coverage and to seek protection against different risks. For cultivation, these risks are related to the agricultural business process and to external risk events. An operational risk assessment allows one to identify the limits of environmental and financial sustainability. Specifically, in oil palm cultivation, the characterisation of the associated risk remains a challenge from a technological perspective. To advance in this direction, researchers have used different technologies, including spectral aerial images, unmanned aerial vehicles to construct a vegetation index, intelligent augmented platforms for real-time monitoring, and adaptive fuzzy models to estimate operational risk. In line with these technological developments, in this article we propose a framework for the estimation of the risk assessment associated with the disease of Lethal Wilt (LW) in oil palm plantations. Although our purpose is not to predict lethal wilt, since the framework starts from the result of a prediction model, a model to detect LW in an early stage is used for the demonstration. For the implementation of the prediction model, we use a novel deep learning system based on two neural networks. This refers to a case study conducted at UNIPALMAS. We show that the suitability of our system aims to evaluate operational risks of LW with a confidence level of 99.9% and for a period of 6 months.Item Embargo A New Wavelet Completed Local Ternary Count (WCLTC) for Image Classification(IEEE, 2021-11-01) Rassem, Taha H.; Alkareem, Fatimah A.; Mohammed, Mohammed Falah; Makbol, Nasrin M.; Sallam, AmerTo overcome noise sensitivity and increase the discriminative quality of the Local Binary Pattern, a Completed Local Ternary Count (CLTC) was developed by combining the Local Ternary Pattern (LTP) with the Completed Local Binary Count (CLBC) (LBP). Furthermore, by integrating the proposed CLTC with the Redundant Discrete Wavelet Transform (RDWT) to construct a Wavelet Completed Local Ternary Count, the proposed CLTC’s discriminative property is improved (WCLTC). As a result, more accurate local texture feature capture inside the RDWT domain is possible. The proposed WCLTC is utilised to perform texture and medical image classification tasks. The WCLTC performance is evaluated using two benchmark texture datasets, CUReT and Outex, as well as three medical picture databases, 2D Hela, VIRUS Texture, and BR datasets. With several of these datasets, the experimental findings demonstrate a remarkable classification accuracy. In several cases, the WCLTC performance outperformed the prior descriptions. With the 2D Hela, CUReT, and Virus datasets, the WCLTC achieves the highest classification accuracy of 96.91%, 97.04 percent, and 72.89%, respectively.Item Metadata only A preliminary study on crop classification with unsupervised algorithms for time series on images with olive trees and cereal crops(Springer, 2020-08-29) Rivera, Antonio Jesus; Perez-Godoy, Maria Dolores; Elizondo, David; Deka, Lipika; del Jesus, Maria JoseItem Open Access A reinforcement learning based dynamic multi-objective constrained evolutionary algorithm for open-pit mine truck scheduling(IEEE, 2023-09) Qiu, Junxiang; Li, Changhe; Yang, ShengxiangAiming at the truck scheduling problem in the open-pit mine scenario, a truck scheduling model based on real-time ore blending is established, and an adaptive evolution algorithm for truck scheduling based on DCNSGA-III is proposed. In the established scheduling model, the real-time grade variance of the crushing plant is minimized as one of the optimization objectives, and the Q-learning algorithm is introduced to adaptively select one of the most effective operators during the search process. Experiments show that the proposed method can effectively control the grade fluctuation of the ore flow and better scheduling schemes are obtained in comparison with algorithms equipped with the traditional search operator selection methods.Item Metadata only Abstraction Based Domain Ontology Extraction for Idea Creation(IEEE, 2013) Jing, D. L.; Yang, Hongji; Tian, Y. C.Item Metadata only Accelerating lean practice training using virtual reality.(2008) Khalil, R. A. (Riham A.); Stockton, David; Wright, N.; Gillis, C.Item Open Access Accelerating the Transition to a Circular Plastic Economy in Nigeria through 3D Printing Technology: Investigating Knowledge and Capacity in Universities(2023-06) Okoya, Silifat Abimbola; Ajala, Olubunmi; Kolade, Oluwaseun; Oyinlola, M. A.Item Metadata only Access control mechanism for mobile ad hoc network of networks.(IEEE Computer Society, 2010-03) Al-Bayatti, Ali Hilal; Zedan, Hussein; Siewe, FrancoisItem Metadata only Access network selection using combined fuzzy control and MCDM in heterogeneous networks.(2007) Alkhawlani, Mohammed; Ayesh, Aladdin, 1972-Item Metadata only Accessibility 2.0: people policy and processes.(ACM, 2007) Kelly, Brian; Brown, Stephen C.; Sloan, David; Lauke, Patrick; Ball, Simon; Seale, JaneItem Open Access Acoustic scene classification: from a hybrid classifier to deep learning(2017-11-16) Vafeiadis, Anastasios; Kalatzis, Dimitrios; Votis, Konstantinos; Giakoumis, Dimitrios; Tzovaras, Dimitrios; Chen, Liming; Hamzaoui, RaoufThis report describes our contribution to the 2017 Detection and Classification of Acoustic Scenes and Events (DCASE) challenge. We investigated two approaches for the acoustic scene classification task. Firstly, we used a combination of features in the time and frequency domain and a hybrid Support Vector Machines - Hidden Markov Model (SVM-HMM) classifier to achieve an average accuracy over 4-folds of 80.9% on the development dataset and 61.0% on the evaluation dataset. Secondly, by exploiting dataaugmentation techniques and using the whole segment (as opposed to splitting into sub-sequences) as an input, the accuracy of our CNN system was boosted to 95.9%. However, due to the small number of kernels used for the CNN and a failure of capturing the global information of the audio signals, it achieved an accuracy of 49.5% on the evaluation dataset. Our two approaches outperformed the DCASE baseline method, which uses log-mel band energies for feature extraction and a Multi-Layer Perceptron (MLP) to achieve an average accuracy over 4-folds of 74.8%.