1,723,126 research outputs found
Control of Transonic Shock Wave Oscillation over a Supercritical Airfoil
No full textIn the present study, a numerical investigation is carried out on the aerodynamic performance of a
supercritical airfoil RAE 2822. Transonic flow fields are considered where self-excited shock wave
oscillation prevails. To control the shock oscillation, a passive technique in the form of an open
rectangular cavity is introduced on the upper surface of the airfoil where the shock wave oscillates.
Reynolds Averaged Navier-Stokes (RANS) equations have been used to predict the aerodynamic
behavior of the baseline airfoil and airfoil with cavity at Mach number of 0.729 and at angle of attack
of 5˚. The aerodynamic characteristics of the baseline airfoil are well validated with the
available experimental data. It is observed that the introduction of a cavity around the airfoil upper
surface can completely stop the self-excited shock wave oscillation and successively improve
the aerodynamic characteristics
Control of Transonic Shock Wave Oscillation over a Supercritical Airfoil
No full textIn the present study, a numerical investigation is carried out on the aerodynamic performance of a
supercritical airfoil RAE 2822. Transonic flow fields are considered where self-excited shock wave
oscillation prevails. To control the shock oscillation, a passive technique in the form of an open
rectangular cavity is introduced on the upper surface of the airfoil where the shock wave oscillates.
Reynolds Averaged Navier-Stokes (RANS) equations have been used to predict the aerodynamic
behavior of the baseline airfoil and airfoil with cavity at Mach number of 0.729 and at angle of attack
of 5˚. The aerodynamic characteristics of the baseline airfoil are well validated with the
available experimental data. It is observed that the introduction of a cavity around the airfoil upper
surface can completely stop the self-excited shock wave oscillation and successively improve
the aerodynamic characteristics
Defects Inspection in Polycrystalline Solar Cells Electroluminescence Images Using Deep Learning
No full textSolar cells defects inspection plays an important role to ensure the efficiency and lifespan of photovoltaic modules. However, it is still an arduous task because of the diverse attributes of electroluminescence images, such as indiscriminative complex background with extremely unbalanced defects and various types of defects. In order to deal with these problems, this paper proposes a new precise and accurate defect inspection method for photovoltaic electroluminescence (EL) images. The proposed algorithm leverages the advantage of multi attention network to efficiently extract the most important features and neglect the nonessential features during training. Firstly, we designed a channel attention to exploit contextual representations and spatial attention to effectively suppress background noise. Secondly, we incorporate both attention networks into modified U-net architecture and named it multi attention U-net (MAU-net) to extract effective multiscale features for defects inspection. Finally, we propose a hybrid loss which combines focal loss and dice loss aiming to solve two problems: a) overcome the class imbalance problem, and b) allowing the network to train with irregular image labels for some complex defects. The proposed multi attention U-net is evaluated on real photovoltaic EL images datasets using 5-fold cross validation technique. Experimental results demonstrate that the proposed network can segment and detect various complex defects correctly. The proposed method achieved the mean intersection over-union (m-IOU) of 0.699 and F-measure of 0.799 which outperforms the previous methods
KPJ Healthcare Berhad / Nurul Fariha Abd Rahman Muhammad
No full textMy experience with KPJ Healthcare Berhad, which lasted for a total of six months, was a very memorable that I will never forget. During the course of the journey, I was given the opportunity to work in the Group Human Resources Management (GHRM) as this was in accordance with the program I was pursuing for my degree. In GHRM, I was assigned to the Human Resource Development, which was comprised of a variety of units. This report is being written for the internship phase, and it is absolutely essential for all the students undergoing industrial training regardless of the course. The information contained in this report was compiled based on the experience that was gained throughout the course of the training and based on both the Annual Report and the website of KPJ Healthcare Berhad. The first part of this report is about the student, and then it talks about the company. After that, I talked about the services KPJ offers, and then I gave a brief summary of my training reflection. The next part is a SWOT analysis of KPJ Healthcare Berhad, followed by my thoughts and suggestions and it ends with a conclusion. The SWOT Analysis is based on the problems I addressed and the things I observed during my six-month training at the company. Also, both my supervisor and my advisor helped me figure out how to do this SWOT Analysis as the main goal of training is to learn while working in a real-world setting and put what I have learned in university to use in real-world situations
Capitalizing on self-supervision and pre-trained models in computer vision
No full textThis thesis addresses the overarching challenge of advancing computer vision tasks under the constraints of limited labeled data and the imperative to capitalize on pre-existing knowledge encoded in pre-trained models. By exploring three distinct computer vision tasks - classification, regression, and segmentation - this work presents diverse frameworks aimed at transcending the conventional boundaries imposed by data scarcity and task-specific methodologies. The first focus lies on Unsupervised Domain Adaptation (UDA) in visual recognition, a critical endeavor in bridging disparate visual domains for robust real-world performance. Existing approaches in UDA typically necessitate manual adaptation to specific backbone architectures, hindering adaptability over time as methods become outdated with evolving architectures. To circumvent this limitation, this thesis proposes a novel approach termed Adversarial Branch Architecture Search for UDA (ABAS). ABAS addresses the lack of target labels by employing a data-driven ensemble approach for model selection and explores auxiliary adversarial branches to drive domain alignment. Extensive validation on standard visual recognition datasets demonstrates ABAS's efficacy in enhancing modern UDA techniques, robustly yielding superior performances across diverse domains. In the realm of regression tasks, the thesis delves into collaborative human pose forecasting, an understudied domain with the potential for improved performance through exploiting the correlated motion patterns of interacting individuals. By revisiting prevalent single-person practices and tailoring them to the collaborative setting, significant advancements are achieved. Notably, the integration of frequency input representations, space-time separable interaction encodings, and fully-learnable interaction adjacencies into a Graph Convolutional Network (GCN) framework showcases promising results. Furthermore, a novel initialization procedure for spatial interaction parameters enhances both performance and stability, culminating in a substantial performance boost over state-of-the-art methods on benchmark datasets. Lastly, the thesis tackles semantic segmentation in autonomous driving scenarios, leveraging the unique capabilities of event cameras for low-latency operation in challenging lighting conditions. We introduce OVOSE, the first open-vocabulary semantic segmentation approach explicitly tailored for event-based data. OVOSE leverages knowledge distillation from pre-trained image-based models and synthetic event data to enhance segmentation performance. Additionally, we propose a novel dissimilarity network to recalibrate mask loss, mitigating the effects of sub-optimal reconstructions and enabling precise fine-tuning of the segmentation model. Through this novel approach, OVOSE demonstrates superior performance in dynamic environments, outperforming existing conventional image-based models and state-of-the-art methods in unsupervised domain adaptation for event-based semantic segmentation. In summary, this thesis presents a holistic approach to computer vision tasks, unifying disparate methodologies under the common goal of leveraging pre-trained models and limited labels to achieve superior performance across diverse domains. By addressing specific challenges within classification, regression, and segmentation tasks, the proposed frameworks contributes towards advancing the frontier of computer vision in real-world applications
Building Sustainable Supply Chain Resilience: Insights From a Mixed‐Method Study
No full textGrowing concerns regarding climate change and extreme weather events have spurred heightened interest among supply chain professionals, researchers, and policymakers, leading to increased focus on supply chain resilience. This study aims to develop a model specifically geared toward enhancing supply chain endurance and contribute to the ongoing debate on supply chain resilience. Employing a mixed-method approach, the research initially utilizes the qualitative methodology to delineate the facets of supply chain endurance. Subsequently, through a multiple cross-sectional survey design, the study empirically examines the relationship between supply chain endurance, a firm's supply chain resilience, and community resilience. The results engender discussions on fortifying firms' supply chain endurance by cultivating adaptive leadership, adaptability, visibility, flexibility, collaboration, redundancy, and conditioning. This research underscores the significance of nurturing supply chain endurance capabilities in bolstering a firm's sustainable supply chain resilience and the resilience of the broader community
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
Role of intermediate states in protein folding and misfolding.
No full textMost proteins fold into their native structure through defined pathways which involve a limited number of transient intermediates. Intermediates play a relevant role in the folding process; many diseases of genetic nature are in fact coupled with protein misfolding, which favours formation of stable inactive intermediate species of a protein. This review describes a number of diseases originated from protein misfolding and briefly discusses the mechanism(s) responsible, at molecular level, for these pathologies. It is also envisaged the native ⇄ molten globule transition since sometimes the conversion of the native form into a compact intermediate state permits a protein to carry out distinct physiological functions inside the cell. A non-native compact form of cyt c, for example, is involved in the programmed cell death (apoptosis) after that the protein is released from the mitochondrion; in addition, non-native forms of the protein are involved in some of the disorders attributed to amyloid formation
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