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Experimental and Numerical Study on the Trailing Edge Cutback Cooling Characteristics with POD Analysis

Journal of Thermal science 2025年第3期34卷 834-849页

作者： LIU Jiajie WANG Pengfei WANG Pei LIU Jun DU Qiang WANG Haohan WANG Zhiguo SHEN Xin ZHU Junqiang Key Lab of Light-duty Gas-turbine Institute of Engineering TermophysicsChinese Academy of Sciences University of Chinese Academy of Sciences National Key Laboratory of Science and Technology on Advanced Light-duty Gas-turbine Research Center of Fluid Machinery Engineering and Technology Jiangsu University

This paper investigates the film cooling characteristics and flow structure of trailing edge cutback in turbine vanes,and explains the unsteady flow mixing mechanism of this cooling structure using Proper Orthogonal Decomposition（POD） *** Delayed Detached Eddy Simulation（DDES） turbulence model was used to obtain detailed information about the velocity and temperature field for the POD *** verify the accuracy of the numerical results,fast-response Pressure Sensitive Paint（PSP） and high-frequency Particle Image Velocimetry（PIV） experiments were also conducted,and the results achieved good *** the blowing ratio increases,the effectiveness η of the cutback's film cooling exhibits a "increase-decrease-increase" trend,with η reaching its minimum point at around a blowing ratio(Mslot) of *** kinds of coherent vortex structures are observed in the flow field at different blowing *** to the analysis using the POD method,the first and second order modes of a Karman-like vortex street are observed in the vicinity of the wall at various blowing *** coherent flow structure is directly related to the mixing intensity between the mainstream gas and the *** Mslot=0.75,these modes had the highest energy ratio and formed a stable dominant coherent structure in the flow *** the blowing ratio increases,the main characteristic modes in the temperature field gradually change,and the mode appears crescent-shaped when the effectiveness of film cooling is at its *** paper combines the vortex structure of the flow field to explain the flow field feature distribution at the lowest effectiveness point η and analyzes its impact on the film cooling characteristics of the protected surface.

关键词： film cooling Proper Orthogonal Decomposition (POD) shedding vortices trailing edge cutback unsteady flow mixing

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Artificial Intelligence Based Dynamic Approach to Visualize the Graphs 1

Artificial Intelligence Based Dynamic Approach to Visualize ...

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1st International Conference on Computational Intelligence, Communication technology and Networking, CICTN 2023

作者： Garg, Harshita Gupta, Ishika Kumar, Kapil Kaur, Baljeet Pundir, Divya Meerut Institute of Engineering & Technology Department of Computer Science and Engineering Meerut India

ISBN: (纸本)9798350338027

Understanding graph algorithms for beginners is the biggest nightmare for them. To conquer that fear we have decided to build a website which will help them to understand graph algorithms in a fun and easy way. It is a visualizer website for Graph Algorithms, where you can see a detailed explanation and animations of important algorithm runtime and build intuition. Graph Algorithm Visualizer allows you to easily construct graphs and step-by-step observe execution of algorithms on them. It visualizes different types of graph algorithms such as Prim's Algorithm, Kruskal Algorithm, DFS etc. Our aim is to build a project which will work for a teaching and learning tool. In learning mode, we are providing a detailed visualization of graph-based algorithms, such as Kruskal, Prim's algorithm as well as each step will be verified against the algorithm and results will be provided at a time. We have confidence that our project will help many students to understand graph-based algorithms at a deeper level and improve their knowledge of graph algorithms. This model is especially built to help faculty to teach graph algorithms in a creative and fun way for students. © 2023 IEEE.

关键词： Visualization

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Outlier detection in maize field using Isolation Forest: A one-class classifier 1

Outlier detection in maize field using Isolation Forest: A o...

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1st IEEE International Conference on Networking and Communications, ICNWC 2023

作者： Michael, J Justina Thenmozhi, M. SRM Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350336009

In agriculture, manual weed identification remains a challenge for farmers and agriculturalists. Recent technologies address the problem of weeds through several methods among which outlier detection is one powerful technique. Outlier detection is an effective weed identification method because it builds the model only by training the positive samples, hence known as one-class classification (OCC). In the context of agriculture, all crops except the target crop are considered to be weeds. This makes OCC a suitable method to identify weeds. In this work, we build OCC-IF-WD (One-Class Classification using Isolation Forest for Weed Detection) model that treats the target class as an inlier and all other classes as outliers. To train this model, an original dataset from Kaggle is used. Isolation Forest, which is a one-class classifier was used to build the model. OCC-IFWD recognizes maize as an inlier and all its associated weeds as outliers. The model was trained with 1082 images. The model was tested against various categories of outliers to examine its performance level. The results show the highest accuracy of 90.2% when the model is tested to differentiate between maize (inlier) and blackgrass (outlier). Also, a precision of 87%, recall of 94%, and Fl-score of 91% were recorded from the model. © 2023 IEEE.

关键词： Crops

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An IoT Solution for Enhanced Tray Management Systems in Food Service and Industrial Settings

An IoT Solution for Enhanced Tray Management Systems in Food...

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2024 IEEE International Conference on Signal Processing and Advance research in Computing, SPARC 2024

作者： Chaudhary, Vipin Kumar Singh, Suchita Singh, Nagendra Pratap Afreen, Sana Yadav, Manmohan Singh Gupta, Durgesh Kumar Lovely Professional University Dept. of Computer Science and Engineering Punjab Phagwara India Buddha Institute of Technology Dept. of Computer Science and Engineering Uttar Pradesh Gorakhpur India Dr. B R Ambedkar National Institute of Technology Dept. of Computer Science and Engineering Punjab Janlandhar India Telangana Hyderabad India Sharda University Dept. of Computer Science and Engineering Uttar Pradesh Greater Noida India Institute of Engineering and Technology Dept. of Computer Science and Engineering Uttar Pradesh Sitapur India

ISBN: (纸本)9798350385199

This study introduces TraySmart, a cutting-edge Internet of Things (IoT) tool meant to completely transform the effectiveness and administration of tray systems in industrial, food court, and cafeteria environments. TraySmart employs cutting-edge technologies like weight sensing, RFID tag reading, temperature monitoring, NFC for user interaction, and Wi-Fi Bluetooth for data transmission to tackle the widespread issues associated with tray management, such as inefficient operations, concerns about food safety, and less-than-ideal user experiences. The weight-sensing feature of the device makes it possible to identify objects on the tray and monitor the contents of the tray in real time. Reading RFID tags makes identifying and tracking items easier, improving inventory control and lowering loss. Food safety and quality assurance depend on food items maintaining their ideal temperatures, which is ensured via temperature monitoring. Furthermore, NFC technology enhances the eating experience by enabling users to engage with the smartphone for payments or feedback. The communication options on the device guarantee smooth data transfer to a central system, facilitating thorough data analysis and system optimization. This essay covers the creativity, design, and development of the TraySmart prototype, the difficulties faced, and the strategies used in the process. The report assesses TraySmart's effect on operational efficiency, food safety standards, and customer happiness by carefully examining the outcomes of pilot testing. With its scalable solution to improve tray management systems, TraySmart marks a significant leap in the application of IoT technology in the food service and industrial sectors. The results highlight how IoT gadgets like TraySmart have the power to revolutionize operational procedures and service delivery, establishing a new benchmark for innovation in the industry. © 2024 IEEE.

关键词： Customer satisfaction

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Enhanced Skin Cancer Prediction with Analysis Using Machine Learning Algorithms

Enhanced Skin Cancer Prediction with Analysis Using Machine ...

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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Duraisamy, Premkumar Dhivakar, J. Adhish Sylwesdhan Prabhu, F. Ajeshbose, J.S. Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350313987

The article discusses about a number of machine learning techniques that can be used for detecting skin cancer. This research study aims to assist the clinicians to precisely detect skin cancer in a patient and begin therapy at an early stage. Several algorithms, including XG Boost Classifier, Random Forest, and Logistic Regression, CNN and KNN algorithm, were used to train and test the dataset provided by ***. This study considered 11 attributes, trained and tested the dataset from the source, and employed multiple machine learning techniques. When compared to other methods, the voting ensemble approach produced 85.02 % accuracy. © 2023 IEEE.

关键词： Classification Kaggle Dataset Machine learning Skin Care

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Ensemble based Dimensionality Reduction for Intrusion Detection using Random Forest in Wireless Networks 5

Ensemble based Dimensionality Reduction for Intrusion Detect...

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5th International Conference on Smart Systems and Inventive technology, ICSSIT 2023

作者： Suhana, S. Karthic, S. Yuvaraj, N. Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9781665474672

In the present digital era, the evolution of internet technologies increases rapidly and as a result large number of devices is connected with the public network. Due to this, huge volume of data is been generated and transmitted over the network. Similarly the attacker has formulated large number of ways to access to information on the network. Due to the availability of internet, almost every device are interconnected and are exposed to data breach. Hence Intrusion Detection System (IDS) are mandatory to ensure the information is secure from the attackers. As technology is developing the attackers identifies a new technique to breach the secure data. Machine Learning approach is been employed in different domain and it has given better results in terms of performance and accuracy. In order to design an effective IDS, this study utilizes machine leaning models. This article has proposed an ensemble-based hybrid approach for intrusion detection in networks. In the initial stage, the important features are extracted using Blended Linear Discriminant Analysis (BLDA). Further the dimensionality reduced dataset are used to detect intrusion using Random Forest Classifier. Here, two benchmark datasets namely NSL-KDD and UNSW-NB15 are used to evaluate the potential of the proposed method. To prove the effectiveness of our approach, the proposed scheme is compared with classical LDA, PCA and PLS based feature selection schemes where the presented method provides. The accuracy of the proposed method is 90.12 % and 91.0 % for NSL-KDD and UNSW-NB15 datasets respectively. The results clearly shows that our proposed method provides considerable improvement in performance of IDS. © 2023 IEEE.

关键词： Intrusion detection

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A Comprehensive Exploration of Noise Models, Blur Models, Filters and Restoration Techniques

A Comprehensive Exploration of Noise Models, Blur Models, Fi...

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2023 Intelligent Computing and Control for engineering and Business Systems, ICCEBS 2023

作者： Malathy, S. Prasanna Devi, S. Rajasekar, V. Srm Institute of Science and Technology Department of Computer Science and Engineering Vadapalani India

ISBN: (纸本)9798350394580

With an objective of restoring a clear image from a distorted version, image restoration is necessary for sophisticated image processing. It employs a mathematical model to address degradation during image acquisition. Noise plays a substantial part in the deterioration of images during their prisoner or transmission. To attack this issue, there exists a wide array of algorithms and filtering ways, each carrying its own set of hypotheticals, advantages, and limitations, all depending on previous knowledge of the noise. Image smoothing is a dynamic and massively retained process in replica processing. The ideal of the conventional deed is to present a compact overview of the most efficacious restoration miniatures attainable on the inquiry. In this study, we will explore and assess the pros and cons linked to different image restoration styles. © 2023 IEEE.

关键词： Blurring Techniques Degradation model Filters Noise Models Restoration Techniques

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Pathological Analysis of Blood Cells Using Deep Learning Techniques

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Recent Advances in computer science and Communications 2022年第3期15卷 397-403页

作者： Ranga, Virender Gupta, Shivam Agrawal, Priyansh Meena, Jyoti Department of Computer Engineering National Institute of Technology Haryana Kurukshetra India Department of Computer Science and Engineering Indian Institute of Information Technology Haryana Sonepat India

Introduction: Pathologists are majorly concerned with detecting the diseases and helping the patients in their healthcare and well-being. The present method used by pathologists for this purpose is manually viewing the slides using a microscope and other instruments. However, this method has a number of limitations such as there is no standard way of diagnosis, there are certain chances of human errors and besides, it burdenizes the laboratory personnel to diagnose a large number of slides on a daily basis. Methods: The slide viewing method is widely used and converted into digital form to produce high resolution images. This enables the area of deep learning and machine learning to get an insight into this field of medical sciences. In the present study, a neural based network has been proposed for classification of blood cells images into various categories. When an input image is passed through the proposed architecture and all the hyper parameters and dropout ratio values are applied in accordance with the proposed algorithm, then the model classifies the blood images with an accuracy of 95.24%. Result: After training the models on 20 epochs. The plots of training accuracy, testing accuracy and corresponding training loss, and testing loss for the proposed model is plotted using matplotlib and trends. Discussion: The performance of the proposed model is better than the existing standard architectures and other works done by various researchers. Thus, the proposed model enables the development of pathological system which will reduce human errors and daily load on laboratory personnel. . This can also in turn help the pathologists in carrying out their work more efficiently and effectively. Conclusion: In the present study, a neural based network has been proposed for classification of blood cells images into various categories. These categories have significance in the medical sciences. When input image is passed through the proposed architecture and all the hyp

关键词： Diagnosis

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A nonlinear recurrent encoders for early detection of strep throat infection to prevent acute rheumatic fever

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Journal of Ambient Intelligence and Humanized Computing 2024年第4期15卷 2201-2213页

作者： Kumar, K. Antony Belinda, M. J. Carmel Mary Kumar, V. Dhilip Rajan, Jerlin Francy Arif, Muhammad Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai India Department of Medical Surgical Nursing Dr. Jeyashekaran Medical Trust Nagercoil India Superior University Lahore Pakistan

Rheumatic Fever or Acute Rheumatic Fever (RF) and Rheumatic Heart Disease (RHD) are commonly suffered by people of all ages around the world. RF usually begins with strep throat infections, skin problems, joint pain etc. Clinical tests and medical informatics are useful to save patients from Streptococcus Group-A bacteria using medical treatments and detection mechanism. Clinical data based statistical analysis approaches assist medical diagnosis model in detecting diseases based on standard decision. Standard decision making frameworks are effective when using these approaches. Nevertheless, existing mechanisms cannot detect or predict the incidence of strep throat infection at an earlier stage based on unclear datasets or complex datasets. The recent computational healthcare informatics systems use Machine Learning (ML) and Deep Learning (DL) techniques, but they are not sufficient in terms of their field integration. The proposed work implements a nonlinear recurrent auto encoder using complex data evaluation procedures to accurately detect and predict the strep throat infections accurately. In this regard, the proposed model builds on the technical features of Denoised Variational Stacked Auto Encoders (DVSE), nonlinear regression computations and Long Shot Term Memory (LSTM) to perform recurrent data analysis practices. Compared with the existing techniques such as Blood Serum Content Analysis model (BSCA), Smartphone based Strep Throat Detection (SSTD) approach and Logistic Regression Based RF Detection approach (LRRF), the multi-level medical data evaluation (throat images and blood samples) provides 10–15% more accuracy for strep throat detection. As shown in the implementation section, the proposed nonlinear recurrent system has the potential to detect strep throat infections early. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Diagnosis

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A cooperative deep learning model for fake news detection in online social networks

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Journal of Ambient Intelligence and Humanized Computing 2023年第4期14卷 4451-4460页

作者： Mallick, Chandrakant Mishra, Sarojananda Senapati, Manas Ranjan Department of Computer Science and Engineering Biju Patnaik University of Technology Odisha Rourkela769015 India Department of Computer Science and Engineering Indira Gandhi Institute of Technology Odisha Sarang759146 India Department of Information Technology Veer Surendra Sai University of Technology Odisha Burla768018 India

Fake news, which considers and modifies facts for virality objectives, causes a lot of havoc on social media. It spreads faster than real news and produces a slew of issues, including disinformation, misunderstanding, and misdirection in the minds of readers. To combat the spread of fake news, detection algorithms are used, which examine news articles through temporal language processing. The lack of human engagement during fake news detection is the main problem with these systems. To address this problem, this paper presents a cooperative deep learning-based fake news detection *** suggested technique uses user feedbacks to estimate news trust levels, and news ranking is determined based on these values. Lower-ranked news is preserved for language processing to ensure its validity, while higher-ranked content is recognized as genuine news. A convolutional neural network (CNN) is utilized to turn user feedback into rankings in the deep learning layer. Negatively rated news is sent back into the system to train the CNN model. The suggested model is found to have a 98% accuracy rate for detecting fake news, which is greater than most existing language processing based *** suggested deep learning cooperative model is also compared to state-of-the-art methods in terms of precision, recall, F-measure, and area under the curve (AUC). Based on this analysis, the suggested model is found to be highly efficient. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Social networking (online)

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