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Experimental design for a novel co-flow jet airfoil

Advances in Aerodynamics 2023年第1期5卷 630-652页

作者： Hao Jiang Weigang Yao Min Xu School of Astronautics Northwestern Polytechnical UniversityXi’an 710072China Faculty of Computing Engineering and MediaDe Montfort UniversityLeicester LE19BHUK

The Co-flow Jet(CFJ)technology holds significant promise for enhancing aerodynamic efficiency and furthering decarbonization in the evolving landscape of air *** aim of this study is to empirically validate an optimized CFJ airfoil through low-speed wind tunnel *** CFJ airfoil is structured in a tri-sectional design,consisting of one experimental segment and two stationary segments.A support rod penetrates the airfoil,fulfilling dual roles:it not only maintains the structural integrity of the overall model but also enables the direct measurement of aerodynamic forces on the test section of the CFJ airfoil within a two-dimensional wind *** parallel,the stationary segments are designed to effectively minimize the interference from the lateral tunnel *** experimental results are compared with numerical simulations,specifically focusing on aerodynamic parameters and flow field *** findings reveal that the experimental framework employed is highly effective in characterizing the aerodynamic behavior of the CFJ airfoil,showing strong agreement with the simulation data.

关键词： Co-flow jet Wind tunnel experiment Flow control Wall effect

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Traffic flow management by detecting and estimating vehicles density based on object detection model

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Neural computing and Applications 2024年第19期36卷 11495-11505页

作者： Said, Yahia Alassaf, Yahya Alsariera, Yazan Ghodhbani, Refka Saidani, Taoufik Ben Rhaiem, Olfa Makhdoum, Moayad Khaled Department of Electrical Engineering College of Engineering Northern Border University Arar Saudi Arabia Department of Civil Engineering College of Engineering Northern Border University Arar Saudi Arabia College of Science Northern Border University Arar Saudi Arabia Faculty of Computing and Information Technology Northern Border University Rafha Saudi Arabia

The huge growth in the number of vehicles is causing serious traffic management problems. Existing roads must handle traffic more than expected which presents serious challenges including congestion and safety. Intelligent traffic management systems were proposed as a solution to solve such problems. This intelligent system is charged to estimate vehicle density and manage the traffic flow accordingly. A wide variety of sensors was deployed for the traffic management system such as cameras, ultrasonic sensors, radar, infrared sensors, and acoustic sensors. In this work, an intelligent traffic flow management system was proposed based on data provided by public surveillance cameras. For this purpose, a real-time vehicle detection and counting model based on You Look Only Once (YOLO) v6 was deployed. The proposed model was trained and evaluated on different publicly available datasets such as BDD 100K and KITTI. Extensive experimentations proved the efficacy of the proposed model for detecting vehicles while operating under real-time constraints. The proposed traffic flow management system was evaluated using real videos for different traffic scenarios, and good results were achieved. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Ultrasonic cameras

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Multi-Agent System Based on Stacking Technique for Rice Yield Prediction 3

Multi-Agent System Based on Stacking Technique for Rice Yiel...

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3rd IEEE International Conference on Electronic engineering, ICEEM 2023

作者： Seireg, Hayam R. Omar, Yasser M. Elmahalawy, Ahmed Menoufia University Faculty of Electronic Engineering Department of Computer Science and Engineering Menouf Egypt Aastmt Faculty of Computing and Information Technology Department of Computer Science Cairo Egypt

ISBN: (纸本)9798350323511

Artificial intelligence (AI) has expanded its influence across various sectors including education, healthcare, and agriculture. In the agricultural setting, the multiagent system (MAS) is recognized as a powerful tool for optimizing resource allocation, enhancing decision-making processes, and improving overall farm productivity. Accurate prediction of rice yield levels is paramount importance in the agricultural sector. It allows farmers, policymakers, and stakeholders to make informed decisions regarding crop management. Individual machine-learning algorithms (MLA) have been used to predict rice yield levels, but they may not fully exploit the available information. Therefore, the novel system implemented based on stacking techniques to solve complex problems. The objective of this paper is to present an efficient system that leverages MAS based on a novel proposed stacking technique (Extra Trees Classifier (ETC), Random Forest Classifier (RFC), Linear Discriminant Analysis (LDA) and Gaussian Naive Bayes (GNB)) for improving the rice yield level prediction within the agricultural environment. Each algorithm brings its unique approach to handling complex relationships, and modeling class separability. The findings from this study provided valuable insights for decision-making in interconnected sectors and facilitating optimal business planning. The dataset incorporated climate variables such as monthly maximum and minimum temperatures and rainfall. The final result of the dataset consists of 1266 rows and 18 features. The results showed that the novel proposed stacking technique achieved the highest prediction accuracy 87% and the best individual Decision tree classifier obtained 77.5%. The novel proposed stacking technique increases the accuracy by 9.5% compared to the best individual MLA. © 2023 IEEE.

关键词： Multi agent systems

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YOLOv8n-CGW: A novel approach to multi-oriented vehicle detection in intelligent transportation systems

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Multimedia Tools and Applications 2025年第7期84卷 3809-3840页

作者： Berwo, Michael Abebe Fang, Yong Sarwar, Nadeem Mahmood, Jabar Aljohani, Mansourah Elhosseini, Mostafa School of Information and Engineering Chang’an University Shaanxi Xi’an710064 China Department of Computer Science Bahria University Lahore Campus Punjab Lahore54600 Pakistan Department of Information Technology Faculty of Computing and Information Technology University of Sialkot Punjab Sialkot51040 Pakistan College of Computer Science and Engineering Taibah University Yanbu46421 Saudi Arabia Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura35516 Egypt

In the context of Intelligent Transportation Systems (ITS), the role of vehicle detection and classification is indispensable for streamlining transportation management, refining traffic control, and conducting in-depth accident analyses. However, the intricate task of accurately detecting multi-oriented vehicles in diverse scenarios remains a challenge, even with the advancements in ITS. Factors such as vehicle morphology, design, road conditions, structural details, and climatic variables provide a significant part in the detection. To address this, our study introduces an advanced YOLOv8n model, specifically the YOLOv8n-CGW, which is built upon pre-trained deep learning frameworks. This model is meticulously tailored to enhance the detection capabilities for multi-oriented vehicles. Comprehensive evaluations on esteemed datasets like MtV, MERGED, Custom dataset, DAWN, WEDGE, and UA-DETRAC underscore the superiority of our approach over existing models. Notably, our YOLOv8n-CGW model achieves a remarkable mAP of 83.3% and boasts a swift inference time of 0.0019s, setting a new benchmark in the domain. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep neural networks

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Centralized Monitoring for Huge FTTH Network using Code Grating 6

Centralized Monitoring for Huge FTTH Network using Code Grat...

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6th Photonics Meeting 2023, PM 2023

作者： Ahmad, Norhidayah Rahman, M.S. Ahmad Abas, A.H. Selangor Bangi43600 Malaysia School of Engineering Faculty of Computing and Engineering Quest International University Ipoh Perak30250 Malaysia

The Covid-19 has impacted the demand in broadband communication specifically in Fiber to the Home (FTTH). The increase in the demand caused the increase in the number of users in Triple play service during the Movement Control Order (MCO) period. Consequently, the high volume of user has slowed down the internet speed and connectivity. In addition, there is no effective monitoring system to monitor the network health from user end. This study was conducted to design a PON Fiber-to-the-Home (FTTH) network which can serve thousands of users by increasing the capacity of the network and at the same time the network operator will be able to monitor any faulty branch(s) at the user end. By upgrading the new component in PON FTTH such as SOA, AWG and Demultiplexer, the network is able to support up to 30 000 users and also able to monitor fault user branches in FTTH network by using code FBG. © Published under licence by IOP Publishing Ltd.

关键词： Fiber to the x

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Smart Devices Applicable in Transformer Core Manufacturing 4

Smart Devices Applicable in Transformer Core Manufacturing

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4th International Conference on Smart Grid Metrology, SMAGRIMET 2023

作者： Dorer, Adriano Razum, Filip Zupan, Tomislav Janic, Zarko Konjevod, Jure KONČAR-Electrical Engineering Institute Ltd. Zagreb Croatia University of Zagreb Faculty of Electrical Engineering and Computing Croatia

ISBN: (纸本)9798350323597

A number of factories are carrying out the ongoing process of digitalization and automatization of their numerous processes in the interest of speeding up the overall process, reducing the error rate or increasing productivity and/or quality. One of the main components of a power transformer is its core, which is made of thin stacked sheets of steel. Due to the complexity of the manufacturing process and the dimensions of sheets and the overall core, the whole stacking process needs to be precise and have relatively low tolerances. All of this dictates a relatively big number of control parameters and measurements during the core manufacturing. Therefore, it is of importance to devise the methodology and smart devices that can speed up the measurements and help in increasing the measurement quality and worker safety. This paper presents such an approach, by showing custom-built devices for that purpose, together with the accompanying software. © 2023 IEEE.

关键词： Power transformers

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Utilizing Big Data Analytics for Social Media Insights 5

Utilizing Big Data Analytics for Social Media Insights

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Vedhika Srinidhi, V.S. Sanjana, S. Rozario, Neo Joseph Dewan, Sia Gouthaman, P. School of Computing College of Engineering & Technology SRM Institute of Science and Technology Department of Networking and Communications Kattankulathur603203 India School of Computing Faculty of Engineering & Technology SRM Institute of Science and Technology Department of Networking and Communications Kattankulathur603203 India

ISBN: (纸本)9798331518097

Big Data analytics is a rapidly evolving field that offers profound insights into various sectors by enabling the analysis of massive and diverse datasets. This research paper focuses on the application of Big Data techniques to social media and airline networks, leveraging tools such as sentiment analysis, clustering, and data visualization to extract actionable information. The study analyses Twitter data to uncover public sentiment trends and classifies topics using advanced machine learning techniques. Furthermore, the research explores the use of clustering algorithms within the Hadoop ecosystem to process large-scale datasets efficiently. Insights from airline customer feedback and network planning data demonstrate how Big Data analytics can optimize operational efficiency and enhance customer satisfaction. Key findings include the identification of distinct sentiment patterns in social media data, the efficiency of novel clustering techniques in handling high-volume datasets, and the utility of advanced visualization methods to convey complex data. These results have significant implications for industries ranging from social media platforms to the airline sector, providing valuable tools for enhancing decision-making processes and improving user experiences across diverse domains. The study highlights the potential of Big Data analytics to drive innovation and operational excellence in dynamic environments. © 2024 IEEE.

关键词： Airline networks Big Data analytics Clustering algorithms Data visualization Sentiment analysis Social media

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A proposal of Android services generator for inter-connection with ADAS

A proposal of Android services generator for inter-connectio...

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2023 IEEE Zooming Innovation in Consumer Technologies Conference, ZINC 2023

作者： Boskovic, Ognjen Kokinovic, Aleksandar Kenjic, Dusan Kastelan, Ivan University of Novi Sad Faculty of Technical Science Department of Computing and Control Engineering Novi Sad Serbia

ISBN: (纸本)9798350347722

The integration of software in the automotive industry has become increasingly complex and time-sensitive, necessitating the implementation of efficient tools. One such tool is the utilization of a generator. Over the past decade, the implementation of In-Vehicle-Infotainment systems has become ubiquitous, occupying a central position within vehicles. Another system present in vehicles is the Advanced Driver Assistance System. Despite their prevalence, these systems operate in isolation. However, the utilization of Android services and a service-client architecture can facilitate communication between disparate systems through the implementation of various service-client-based protocols. This study proposes the development of a generator for Android services that enable communication between Advanced Driver Assistance System systems through the use of CommonAPI proxy. The implementation of this generator will expedite the production process of code that integrates these independent systems. © 2023 IEEE.

关键词： Advanced driver assistance systems

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Effects of Genetic Operators on Neural Architecture Search Using Multi-Objective Genetic Algorithm 20

Effects of Genetic Operators on Neural Architecture Search U...

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20th International Joint Conference on Computer Science and Software engineering, JCSSE 2023

作者： Patcharabumrung, Praiwan Jewajinda, Yutana Praditwong, Kata Silpakorn University Faculty of Engineering and Industrial Technology Department of Electrical Engineering Nakhon Pathom Thailand Silpakorn University Faculty of Science Department of Computing Nakhon Pathom Thailand

ISBN: (纸本)9798350300505

This paper presents the effects of crossover and mutation operators on neural architecture search using a multi-objective genetic algorithm. The proposed algorithm employs a dual population approach with non-dominated sorting, namely, elite, and mixed population, to increase the diversity of the search. To evaluate the effect of genetic operators, we use a simple layer-based encoding for VGG-like convolution neural network models that resemble the model. We also present the effects of the initialized populations' diversity on the solutions' quality with three types of genetic operators: crossover, mutation, and crossover with a mutation in two groups of experiments initialized with low and high diversity. The experimental results are reported on the Cifar-10 dataset and compared to the state-of-the-art approach. © 2023 IEEE.

关键词： Genetic algorithms

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Supervised Machine Learning Techniques for Power Consumption Usage Level Prediction 6

Supervised Machine Learning Techniques for Power Consumption...

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6th International Conference on Software engineering and Computer Science, CSECS 2023

作者： Tan, Yi-Fei Zhao, Ge-Zhi Cheeng, Tze-Hang Ooi, Chee-Pun Tan, Wooi-Haw Cheong, Soon-Nyean Multimedia University Faculty of Engineering Cyberjaya Malaysia University of Technology Faculty of of Engineering Chongqing Chongqing China Multimedia University Faculty of Computing and Informatics Cyberjaya Malaysia Multimedia University Faculty of Creative Multimedia Cyberjaya Malaysia

ISBN: (纸本)9798350306378

Electricity is an essential part of modern life and is crucial for many aspects of society and the economy. It is essential for industries to operate and create products, for transportation systems to function, and for communication and information technology to function. As the world continues to become more connected and technologically advanced, the demand for electricity is likely to increase, making it an even more critical resource in the future. Overusing electricity will lead to higher energy bills, which can put a strain on household or business budgets. In this paper, the authors developed a model to predict the level of electricity consumption usage. The model is developed in 2 stages. First stage is to categorize the electricity usage into several levels by using equally divided method. Second stage is to use supervised machine learning techniques to develop a prediction model for the level of electricity consumption. The experimental results indicated that using the Random Forest classifier leads to better performance in terms of accuracy, precision, recall, F1 score, and kappa score. Besides that, the results show that 2-level prediction performed better than 4-level prediction. © 2023 IEEE.

关键词： Forecasting

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