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Numerical analysis of the performance of a three-bladed vertical-axis turbine with active pitch control using a coupled unsteady Reynolds-averaged Navier-Stokes and actuator line model

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Journal of Hydrodynamics 2023年第3期35卷 516-532页

作者： Rui-wen Zhao Angus C.W.Creech Ye Li Vengatesan Venugopal Alistair G.L.Borthwick Laboratory of Multi-function Towing Tank State Key Laboratory of Ocean EngineeringSchool of Naval ArchitectureOcean and Civil EngineeringShanghai Jiao Tong UniversityShanghai 200240China School of Engineering and Physical Sciences Heriot-Watt UniversityEdinburghUK School of Engineering The University of EdinburghEdinburghUK School of Engineering Computing and MathematicsUniversity of PlymouthPlymouthUK

In this paper,we present a numerical model of a vertical-axis turbine(VAT)with active-pitch torque *** model is based upon the Wind and Tidal Turbine Embedded Simulator(WATTES)and WATTES-V turbine realisations in conjunction with the actuator line method(ALM),and uses OpenFOAM to solve the unsteady Reynolds-averaged Navier-Stokes(URANS)equations with two-equation k-εturbulence *** novel pitch-controlled system is based on an even pressure drop across the entire rotor to mitigate against dynamic stall at low tip speed *** numerical model is validated against experimental measurements and alternative numerical predictions of the hydrodynamic performance of a 1:6 scale UNH-RM2 hydrokinetic *** deploying the variable pitch mechanism exhibit improved turbine performance compared to measured data and fixed zero-pitch model ***-wake characteristics are investigated by examining the vorticity distribution near the *** pitch-controlled system is demonstrated to theoretically decrease turbulence generated by turbine rotations,mitigate the intensity of vortex shedding and size of detached vortices,and significantly enhance the performance of a vertical-axis hydrokinetic turbine for rated tip-speed ratios.

关键词： Vertical-axis turbine pitch control hydrodynamic performance unsteady Reynolds-averaged Navier-Stokes(URANS) numerical simulation

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Utilising Machine Learning for Precise State-of-Charge Prediction in Li-Ion Batteries 12

Utilising Machine Learning for Precise State-of-Charge Predi...

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12th IEEE International Conference on Intelligent Systems, IS 2024

作者： Khatri, Avinash Lota, Jaswinder Nepal, Prasanna Amirhosseini, Mohammad Hossein School of Architecture Computing and Engineering University of East London London United Kingdom Nepal Engineering College Department of Electrical and Electronics Engineering Bhaktapur Nepal

ISBN: (纸本)9798350350982

As the popularity of electric propulsion using batteries rises alongside the demand for renewable energy, effective battery management and monitoring are crucial for sustain ability and efficiency in electric vehicles (EVs). The battery monitoring system (BMS) employs IoT/sensor networks to estimate crucial battery metrics like remaining useful life (RUL), state-of-health (SoH), and state-of-charge (SoC), using data on current status, temperature, and voltage. Machine learning (ML) and artificial intelligence (AI) are increasingly utilized to enhance BMS accuracy, addressing challenges like real-time data processing and the accuracy of estimations. This paper investigates the effectiveness of Linear Regression and Random Forest models in estimating SoC. During the hyperparameter tuning phase, the models were optimized using the Grid Search method, and their performance was evaluated at various temperatures: 25°C, 10°C, 0°C, and -10°C. The findings indicate that the models' effectiveness enhances as the temperature increases. The Random Forest model exhibited superior performance at 25°C, achieving an R2 score of 0.99646 and an RMSE score of 0.000264. This paper not only contributes to advancing Li-ion battery monitoring system, but also empowers professionals in this field to harness machine learning capabilities effectively. © 2024 IEEE.

关键词： State of charge

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Corn Plant Counting Using Deep Learning and UAV Images

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IEEE Geoscience and Remote Sensing Letters 2024年 1-5页

作者： Kitano, Bruno T. Mendes, Caio C. T. Geus, Andre R. Oliveira, Henrique C. Souza, Jefferson R. Faculty of Computing Federal University of Uberlêndia Uberlêndia 38408-100 Brazil School of Civil Engineering Architecture and Urban Planning University of Campinas Campinas 13083-970 Brazil

The adoption of new technologies, such as unmanned aerial vehicles (UAVs), image processing, and machine learning, is disrupting traditional concepts in agriculture, with a new range of possibilities opening in its fields of research. Plant density is one of the most important corn (Zea mays L.) yield factors, yet its precise measurement after the emergence of plants is impractical in large-scale production fields due to the amount of labor required. This letter aims to develop techniques that enable corn plant counting and the automation of this process through deep learning and computational vision, using images of several corn crops obtained using a low-cost unmanned aerial vehicle (UAV) platform assembled with an RGB sensor. IEEE

关键词： Computer architecture

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A Machine Learning Framework for Skin Cancer Detection using Transfer Learning 6

A Machine Learning Framework for Skin Cancer Detection using...

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6th IEEE International Conference on Image Processing, Applications and Systems, IPAS 2025

作者： Augustine, Mary Linda Panakal Spanogianopoulos, Sotirios Sharif, Mhd Saeed University of East London School of Architecture Computing and Engineering Department of Computer Science and Digital Technologies London United Kingdom University of Portsmouth School of Electrical and Mechanical Engineering Portsmouth United Kingdom

ISBN: (纸本)9798331506520

This paper uses deep learning algorithms including InceptionV2, InceptionV3, DenseNet, MobileNet, and VGG19 to improve skin cancer detection. This research aims to improve skin cancer diagnosis. This work aims to determine the most effective evolutionary metrics-based technique to recognizing skin cancer, which is comparable to other diseases. Ultimately, our paper aims to create a realistic skin cancer detection system that uses the best deep learning algorithm. This discovery might improve medical diagnostics, leading to earlier diagnosis and improved healthcare outcomes. © 2025 IEEE.

关键词： Adversarial machine learning

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Artificial Intelligence in Concrete Mix Design: Advances, Applications and Challenges

Artificial Intelligence in Concrete Mix Design: Advances, Ap...

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2023 International Conference on Innovation and Intelligence for Informatics, computing, and Technologies, 3ICT 2023

作者： Barbhuiya, Salim Sharif, Mhd Saeed Uel Department of Engineering and Construction London United Kingdom Uel School of Architecture Computing and Engineering London United Kingdom

ISBN: (纸本)9798350307771

This review paper explores the application of Artificial Intelligence (AI) in concrete mix design and its impact on the concrete industry. The traditional approaches to concrete mix design are first discussed, highlighting their limitations. Subsequently, various applications of AI in concrete mix design are presented, including optimal proportioning of concrete mixes, prediction of concrete properties, quality control and assurance, concrete strength prediction and optimisation and durability assessment and enhancement. The benefits and impact of AI in the concrete industry are then examined, emphasising the advantages and benefits of using AI in concrete mix design. However, challenges and limitations related to data availability and quality, interpretability of AI models and integration with existing design practices are also addressed. Finally, the paper concludes with a summary of key findings and recommendations for future research in this field. © 2023 IEEE.

关键词： Durability

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Implementing a Chatbot Music Recommender System based on User Emotion

Implementing a Chatbot Music Recommender System based on Use...

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2023 International Conference on Innovation and Intelligence for Informatics, computing, and Technologies, 3ICT 2023

作者： Mathew, Neenumol Chooramun, Nitish Sharif, Mhd Saeed Uel School of Architecture Computing and Engineering Computer Science and Digital Technologies Department London United Kingdom

ISBN: (纸本)9798350307771

The use of chatbots has become increasingly popular in recent years, as more organisations try to improve and streamline their customer service operations. One area which has been gaining momentum is the use of chatbots for music recommendation. Such systems utilise AI technologies to deliver personalised music recommendations to users via conversational interfaces. Chatbot music recommender systems present several benefits namely, they provide a personalised and natural experience which can be engaging for the users. Moreover, the users can engage in a dialogue whereby the system can better interpret the user context and preferences. This work presents the development of a chatbot personalised music recommender system, based on Natural Language Processing (NLP) techniques, coupled with a web interface that can provide song recommendations based on the user's emotions. © 2023 IEEE.

关键词： Recommender systems

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Real-Time Customer Emotion Analysis in E-Commerce based on Social Media Data: Insights and Opportunities

Real-Time Customer Emotion Analysis in E-Commerce based on S...

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2023 International Conference on Innovation and Intelligence for Informatics, computing, and Technologies, 3ICT 2023

作者： Suresh, Madhav Manohar Chooramun, Nitish Sharif, Mhd Saeed Uel School of Architecture Computing and Engineering Computer Science and Digital Technologies Department London United Kingdom

ISBN: (纸本)9798350307771

In this era of social media, understanding consumer emotions in real-time is crucial for businesses to adapt and enhance their offerings promptly. This study delves into real-time consumer emotion analysis in the e-commerce industry using social media data, propelled by the publicly available GoEmotion dataset sourced from Reddit. This dataset encompasses 58,000 comments meticulously labeled for 28 emotion categories, providing an abundant source for sentiment analysis and emotion classification, especially relevant to the e-commerce sector. Released in 2020, this dataset offers a contemporary view of consumer emotions on social media. Human annotators diligently labeled each comment, resulting in a dataset that accurately mirrors users' emotions across diverse Reddit topics. Advanced machine learning techniques, including convolutional neural networks and Bidirectional Encoder Representations from Transformers (BERT), were employed to categorise the emotions expressed in the comments. The practical result of this research is a machine learning model integrated with a user interface, delivering actionable insights derived from real-time Twitter data to e-commerce companies. This integration allows businesses to tailor their products and services in alignment with consumer sentiments expressed on Twitter. Notably, the model achieved a substantial improvement in macro-average precision (0.54) compared to previous work (0.40). The BERT model demonstrated exceptional proficiency in predicting specific emotions like gratitude, love, and admiration, boasting F1-scores of 0.82, 0.57, and 0.48, respectively. Additionally, the CNN model excelled in predicting 'gratitude,' demonstrating impressive precision, recall, and F1-scores. The study aims to identify the most prevalent emotions and empower businesses to customise their products and services accordingly. © 2023 IEEE.

关键词： Sentiment analysis

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Predicting Shear Capacity of RC Beams Strengthened with NSM FRP Using Neural Networks

Predicting Shear Capacity of RC Beams Strengthened with NSM ...

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2023 International Conference on Innovation and Intelligence for Informatics, computing, and Technologies, 3ICT 2023

作者： Guler, Ozan Ahmed, Husham Abbas, Ali Sharif, Mhd Saeed School of Architecture Computing and Engineering Uel Dept. of Engineering and Construction London United Kingdom Jacobs Uk Ltd Principal- Civil/Structural Engineering London United Kingdom School of Architecture Computing and Engineering Uel Department Computer Science and Digital Technologies London United Kingdom

ISBN: (纸本)9798350307771

This research aims to predict the shear capacity of NSM FRP beams using artificial neural network approach. The study investigates the key considerations and the necessary analysis for this prediction. NSM FRP beams are reinforced concrete beams that are strengthened with near-surface mounted (NSM) fiber-reinforced polymer (FRP) composites. Accurately predicting their shear capacity is important for ensuring their safety and reliability in real-world applications. The neural network method is a machine learning approach that is increasingly used in engineering analysis and design. The study explores how this method can be used to predict the shear capacity of NSM FRP beams and what factors should be taken into account in this analysis. The research also discusses the analytical approach required for this prediction, highlighting the necessary steps for obtaining accurate results. Overall, this study provides valuable insights into the use of the neural network for predicting the shear capacity of NSM FRP beams. The findings can help inform future research and practical applications in the field of structural engineering, contributing to the development of safer and more reliable structures. © 2023 IEEE.

关键词： Forecasting

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Evaluation of Students’ Performance with Facial Capture Avatars in Online English Instruction 18th

Evaluation of Students’ Performance with Facial Capture Ava...

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18th International Conference on Computer Science and Education, ICCSE 2023

作者： Qiu, Yu Wang, Gang Zhang, Qianxi School of Computing and Data Engineering Ningbo Tech University Ningbo China School of Design University of Nottingham Nottingham United Kingdom Department of Architecture and Engineering University of Nottingham Ningbo China

ISBN: (纸本)9789819707904

This article evaluates the possibility of deploying virtual avatars on personal devices in online English instruction, as well as the impact on student-teacher interaction and tacit truancy issues. Based on the correlation analysis and linear regression analysis of the data acquired from the distributed questionnaires, it is possible to conclude that there may be no statistical association between avatars and student-teacher interaction. However, avatars have a positive effect on reducing truancy. Therefore, we consider that the utilization of virtual avatars in computer-assisted language learning provides an innovative and promising method for online instruction. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Association reactions

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Integrating Machine Learning with Concrete Science: Bridging Traditional Testing and Advanced Predictive Modelling

Integrating Machine Learning with Concrete Science: Bridging...

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2024 International Conference on Innovation and Intelligence for Informatics, computing, and Technologies, 3ICT 2024

作者： Barbhuiya, Salim Sharif, Mhd Saeed Department of Engineering and Construction University of East London London United Kingdom Intelligent Technologies Research Group School of Architecture Computing and Engineering London United Kingdom

ISBN: (纸本)9798331533137

This paper thoroughly explores the application of machine learning (ML) in concrete science, bridging traditional testing methods with advanced ML techniques. It begins with an overview of ML fundamentals and their relevance to concrete materials, highlighting ML's transformative potential in enhancing predictive modelling and analysis. The discussion covers various ML techniques, including supervised, unsupervised, and deep learning, along with common algorithms and models used in concrete research. Practical aspects such as data collection methods, preprocessing techniques, and feature engineering specific to concrete science are detailed, illustrating how ML improves the accuracy and efficiency of predicting properties like compressive strength, durability, and workability. The paper also examines challenges such as data quality, model interpretability, and scalability, and discusses future trends, ethical considerations, and the societal impacts of ML applications in advancing sustainable infrastructure. ©2024 IEEE.

关键词： Concrete testing

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