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Effect of the Mixing Structure Parameters of a Self-reflux Burner on Combustion Characteristics and NO_(x)Emission

Journal of Thermal Science 2021年第4期30卷 1224-1236页

作者： XU Qian SHEN Meng SHI Kejian LIU Zhihui AKKURT Nevzat XIONG Yaxuan LIU Lin FENG Junxiao WANG Jiulong School of energy and environmental engineering Shunde Graduate SchoolUniversity of Science and Technology BeijingBeijing 100083China Beijing Key Laboratory of Energy Conservation and Emission Reduction for Metallurgical Industry Beijing 100083China Department of Mechanical Engineering Munzur UniversityTunceli 62000Turkey Key Laboratory of HVAC Beijing University of Civil Engineering and ArchitectureBeijing 100044China School of Mathematics and Physics University of Science and Technology BeijingBeijing 100083China Department of Big Data Technology and Application Development Computer Network Information CenterChinese Academy of SciencesBeijing 100190China

To solve the problem of low efficiency of NO_(x)emission reduction in self-reflux burners,this study is based on the concept of coordinated control of self-reflux burner structural and thermal *** completing the structural design and optimization of thermal parameters,we continue to adjust the two key structural parameters:the nozzle axis distance and the length of the cylindrical section,to minimize NO_(x)*** are the two parameters that chiefly affect the mixing of flue gas and fuel *** results show that increasing nozzle axis distance can delay the mixing of gas and air and create a more uniform oxygen concentration field for the combustion *** maximum combustion temperature is reduced from 1973.65 K to 1935.88 K and the volume fraction of NO_(x)in the flue gas is reduced from 188.08×10^(–6)to 143.47×10^(–6).However,compared with the nozzle axis distance,the length of the cylindrical section of the burner has little effect on the mixing of the flow *** different cylindrical section lengths,the maximum combustion temperature does not change more than 3 K,and the volume fraction of NO_(x)in the flue gas changes within 5×10~(–6).

关键词： self-reflux structure structural parameter control thermal parameter combustion characteristic NO_(x)emission

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Landslide Disaster Assessment with Integration of Seismic, Social Media and Remote Sensing Data

Landslide Disaster Assessment with Integration of Seismic, S...

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International Conference on Signal Processing and Information Security (ICSPIS)

作者： Deepak Rawat M. L. Sharma Roshan Kumar Nishant Saxena Atul Kumar Abdul Kadir Centre of Excellence in Disaster Mitigation and Management Indian Institute of Technology Roorkee Roorkee Uttarakhand India Department of Earthquake Engineering Roorkee Indian Institute of Technology Roorkee Roorkee Uttarakhand India Department of Electronics and Information Technology Miami College Henan University Henan Kaifeng China Department of Architecture and Planning Indian Institute of Technology Roorkee Roorkee Uttarakhand India Computer Science and Engineering Department Indian Institute of Technology Roorkee Roorkee Uttarakhand India

On February 7, 2021, a rockfall in the Rishiganga Valley killed about 200 people. For the integration of seismic, social media, and remote sensing, a case study on this tragedy has been conducted. This study explores the role of social media in managing Himalayan landslides in conjunction with remote sensing data analytics. We look at how these technologies can enhance damage management, quick mobilization, and early warning systems. A literature review and case studies of the successful use of these technologies in other disaster-prone areas will support these suggestions. The intention is to equip decision-makers and experts in disaster management with the practical knowledge they need to effectively prepare for and respond to Himalayan landslide events, saving lives and minimizing the impact of natural hazards.

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Diffusion Unit: Interpretable Edge Enhancement and Suppression Learning for 3D Point Cloud Segmentation

SSRN

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SSRN 2023年

作者： Xiu, Haoyi Liu, Xin Wang, Weimin Kim, Kyoung-Sook Shinohara, Takayuki Chang, Qiong Matsuoka, Masashi Department of Architecture and Building Engineering Tokyo Institute of Technology Tokyo Japan Artificial Intelligence Research Center AIST Tokyo Japan DUT-RU International School of Information Science and Engineering Dalian University of Technology Dalian China Innovation Technology Office Research Center PASCO Corporation Tokyo Japan Department of Computer Science Tokyo Institute of Technology Tokyo Japan

3D point clouds are discrete samples of continuous surfaces which can be used for various applications. However, the lack of true connectivity information, i.e., edge information, makes point cloud recognition challenging. Recent edge-aware methods incorporate edge modeling into network designs to better describe local structures. Although these methods show that incorporating edge information is beneficial, how edge information helps remains unclear, making it difficult for users to analyze its usefulness. To shed light on this issue, in this study, we propose a new algorithm called Diffusion Unit (DU) that handles edge information in a principled and interpretable manner while providing decent improvement. First, we theoretically show that DU learns to perform task-beneficial edge enhancement and suppression. Second, we experimentally observe and verify the edge enhancement and suppression behavior. Third, we empirically demonstrate that this behavior contributes to performance improvement. Extensive experiments and analyses performed on challenging benchmarks verify the effectiveness of DU. Specifically, our method achieves state-of-the-art performance in object part segmentation using ShapeNet part and scene segmentation using S3DIS. Our source code is available at https://***/martianxiu/DiffusionUnit. © 2023, The Authors. All rights reserved.

关键词： Diffusion

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Improvement of Color Image Analysis Using a New Hybrid Face Recognition Algorithm based on Discrete Wavelets and Chebyshev Polynomials

arXiv

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arXiv 2023年

作者： Muhialdeen, Hassan M. Abdulrahman, Asma A. Eleiwy, Jabbar Abed Tahir, Fouad S. Khlaponin, Yurii Al-Sharify, Mushtaq Talib Department of Computer Engineering Aliraqia University 22 Sabaabkar Adamia Baghdad Iraq Department of Applied Sciences University of Technology 52 Alsena street Baghdad Iraq Department of Cybersecurity and Computer Engineering Kyiv National University of Construction and Architecture 31 Povitroflotskyi ave Kyiv03037 Ukraine Department of Communication Technical Engineering Al-Farahidi University Baghdad Iraq

This work is unique in the use of discrete wavelets that were built from or derived from Chebyshev polynomials of the second and third kind, filter the Discrete Second Chebyshev Wavelets Transform (DSCWT), and derive two effective filters. The Filter Discrete Third Chebyshev Wavelets Transform (FDTCWT) is used in the process of analyzing color images and removing noise and impurities that accompany the image, as well as because of the large amount of data that makes up the image as it is taken. These data are massive, making it difficult to deal with each other during transmission. However to address this issue, the image compression technique is used, with the image not losing information due to the readings that were obtained, and the results were satisfactory. Mean Square Error (MSE), Peak Signal Noise Ratio (PSNR), Bit Per Pixel (BPP), and Compression Ratio (CR) Coronavirus is the initial treatment, while the processing stage is done with network training for Convolutional Neural Networks (CNN) with Discrete Second Chebeshev Wavelets Convolutional Neural Network (DSCWCNN) and Discrete Third Chebeshev Wavelets Convolutional Neural Network (DTCWCNN) to create an efficient algorithm for face recognition, and the best results were achieved in accuracy and in the least amount of time. Two samples of color images that were made or implemented were used. The proposed theory was obtained with fast and good results;the results are evident shown in the tables below. © 2023, CC BY.

关键词： Wavelet transforms

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Sentence Bag Graph Formulation for Biomedical Distant Supervision Relation Extraction

arXiv

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arXiv 2023年

作者： Zhang, Hao Liu, Yang Liu, Xiaoyan Liang, Tianming Sharma, Gaurav Xue, Liang Guo, Maozu The School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China The Department of Electrical and Computer Engineering University of Rochester RochesterNY14627 United States The *** HangZhou 310000 China The School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing100044 China The Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture Beijing100044 China

We introduce a novel graph-based framework for alleviating key challenges in distantly-supervised relation extraction and demonstrate its effectiveness in the challenging and important domain of biomedical data. Specifically, we propose a graph view of sentence bags referring to an entity pair, which enables message-passing based aggregation of information related to the entity pair over the sentence bag. The proposed framework alleviates the common problem of noisy labeling in distantly supervised relation extraction and also effectively incorporates inter-dependencies between sentences within a bag. Extensive experiments on two large-scale biomedical relation datasets and the widely utilized NYT dataset demonstrate that our proposed framework significantly outperforms the state-of-the-art methods for biomedical distant supervision relation extraction while also providing excellent performance for relation extraction in the general text mining domain. Copyright © 2023, The Authors. All rights reserved.

关键词： Message passing

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Keyword extraction based on semantic similarity metric and multi-feature computing 7

Keyword extraction based on semantic similarity metric and m...

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7th International Conference on Big Data Computing and Communications, BigCom 2021

作者： Kang, Hui Sun, Xin Feng, Tie Lin, Shuhang Jilin University College of Computer Science and Technology Changchun China Jilin University College of Software Changchun China Beijing ByteDance Technology Co. Ltd. Department of Product R&D and Engineering Architecture Beijing China

ISBN: (纸本)9781665442527

Keyword extraction is an effective method to extract keywords from a text which can express the main content of the text. However, it is difficult to extract useful information quickly from a large number of documents. In this paper, a semantic similarity metric and multi-feature computing based keyword extraction algorithm is presented. First, a semantic similarity algorithm based on WordNet semantic dictionary toward distance, information and property (SSDIPA) is proposed, which quantifies the degree of association between words. Then, a multi-feature tuple (word frequency, word length, word span, word position, semantic similarity) based computing method is presentssd to score the candidate keywords. Finally, the feature decision tree is used to adjust the proportional relationship among the features so that the keyword extraction results can meet the use's subjective preferences. The result shows that the proposed algorithm outperforms other comparison algorithms, and the multi-feature computing based keywords extraction algorithm can improve the accuracy. © 2021 IEEE.

关键词： Decision trees

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A Methodology for Selective Protection of Matrix Multiplications: A Diagnostic Coverage and Performance Trade-off for CNNs Executed on GPUs

A Methodology for Selective Protection of Matrix Multiplicat...

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International Conference on System Reliability and Science (ICSRS)

作者： Javier Fernández Irune Agirre Jon Perez-Cerrolaza Jaume Abella Francisco J. Cazorla Ikerlan Technological Research Center Basque Research and Technology Alliance (BRTA) Spain Computer Architecture-Operating Systems Department Barcelona Supercomputing Center Spain

The ability of CNNs to efficiently and accurately perform complex functions, such as object detection, has fostered their adoption in safety-related autonomous systems. These algorithms require high computational performance platforms that exploit high levels of parallelism. The detection, control and mitigation of random errors in these underlying high computational platforms become a must according to functional safety standards. In this paper, we propose protecting, with a catalog of diagnostic techniques, the most computationally expensive operation of the CNNs, the matrix multiplication. However, this protection entails a performance penalty, and the complete CNN protection may be unaffordable for those systems operating with strict real-time constraints. This paper proposes a three-stage methodology to selectively protect CNN layers to achieve the required diagnostic coverage and performance trade-off: i) sensitivity analysis to misclassification per CNN layers using a statistical fault injection campaign, ii) layer-by- layer performance impact and diagnostic coverage analysis, and iii) selective layer protection. Furthermore, we propose a strategy to effectively compute the achievable diagnostic coverage of large matrices implemented on GPUs. Finally, we apply the proposed methodology and strategy in Tiny YOLO-v3, an object detector based on CNNs.

关键词： Sensitivity analysis Autonomous systems Object detection Detectors Parallel processing Real-time systems Safety

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Enhancing Visual Surveillance on Smart Ships Through Integrated Image Deblurring and Object Detection

SSRN

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SSRN 2024年

作者： Feng, Hui Guo, Jundong Xu, Haixiang Du, Zhe Zhang, Yingju He, Yijing Cao, Guilin Ge, Shuzhi Sam Key Laboratory of High-Performance Ship Technology Wuhan University of Technology Ministry of Education Wuhan China School of Naval Architecture Ocean and Energy Power Engineering Wuhan University of Technology Wuhan430063 China Wuhan University of Technology China Key Laboratory of High Performance Ship Technology of Ministry of Education Wuhan University of Technology Wuhan430063 China Department of Electrical & Computer Engineering National University of Singapore Singapore117576 Singapore

Smart ships commonly utilize visual measurement units, such as cameras, for environmental perception in the maritime domain, aimed at achieving advanced navigation capabilities. Characterized by the influence of wind, wave, and ocean current, smart ships navigating on maritime environment frequently encounter sway and motion disturbances, which subsequently introduce uncertainties to visual sensors, notably leading to motion blur in captured images. In order to mitigate the detrimental impact of motion blur on the visual obstacle detection algorithm employed in smart ships, we introduce IDD-GAN. Unlike conventional approaches, which treat motion deblurring and object detection as separate tasks, IDD-GAN integrates both processes, enabling end-to-end training and prediction through the use of generative adversarial networks. To address varying scales of motion blur caused by different levels of sensor interference, we have developed a multi-scale deblurring discriminator. Empirical results demonstrate the capability to achieve high-performance objectdetection even under conditions of motion blur. © 2024, The Authors. All rights reserved.

关键词： Ships

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Enhanced Bone Fracture Diagnosis in X-rays Using Fine-Tuned DenseNet169 Deep Learning Model

Enhanced Bone Fracture Diagnosis in X-rays Using Fine-Tuned ...

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IEEE International Conference on Multi Topic

作者： Ali Orangzeb Panhwar Shahzad Memon Lachhman Das Dhomeja Mukesh Prasad Asghar Ali Chandio Dr. A. H. S. Bukhari Postgraduate Centre of Information and Communication Technology University of Sindh Jamshoro Pakistan SZABIST University Gharo Department of Computer Science and Digital Technologies Scool of Architecture Compuing and Engineering University of East London United Kingdom Faculty of Engineering and Information Technology University of Technology Sydney Australia Department of Artificial Intelligence Science and Technology (QUEST) Nawabshah Quaid-e-Awam University of Engineering Pakistan

ISBN: (数字)9798331507213

ISBN: (纸本)9798331507220

The classification of bone fractures from radiographs is an important yet challenging task in clinical diagnosis. Diagnosing fractures through X-rays remains difficult for orthopedic specialists due to image quality issues, which can result in errors, misalignments, and potential harm to patients. However, recent advancements in artificial intelligence (AI) and deep learning have revolutionized medical imaging, with state-of-the-art methods now capable of handling 2D and 3D images. This study focuses on deep-learning approaches for the classification and detection of bone fractures in radiograph images and aims to analyze and compare various deep-learning algorithms and techniques used in fracture detection. It also highlights current cutting-edge approaches in this field, providing insights and guidance for future research and practical applications. In this paper, the application of Fine-tuned DenseNet169 for the automated classification of bone fractures in X-ray images is explored. By using deep learning approaches, our method seeks to enhance the accuracy and efficiency of fracture detection. We trained and evaluated the DenseNet169 model on the MURA Stanford dataset and achieved 83% accuracy in distinguishing fractured and non-fractured elbow bones. The model’s performance highlights the potential of DenseNet169 to assist radiologists in clinical settings, promoting better patient outcomes through prompt and reliable fracture diagnosis.

关键词： Deep learning Accuracy Three-dimensional displays X-rays Bones Data models Convolutional neural networks Diagnostic radiography Artificial intelligence Load modeling

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Directivity Improvement of MIMO Array Antenna using Novel Eight Element Array Configuration

Directivity Improvement of MIMO Array Antenna using Novel Ei...

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Advancement in Electronics & Communication Engineering (AECE), International Conference on

作者： Alaa M. Lafta Abdul Razzaq T. Zaboun Shakhlokhon Kosimova Alaa. Shather Hawraa Ali Sabah Haider Alchilibi National University of Science and Technology Dhi Qar Iraq Forensic sciences department Al-Esraa University College Baghdad Iraq Department of Architecture Fergana Polytechnic Institute Fergana Uzbekistan Department of Computer Engineering Technology Al Kitab University Altun Kopru Kirkuk Iraq Medical Laboratory Techniques Department Al-Farahidi University Baghdad Iraq

In this study, the Directivity of the Microstrip Patch MIMO Antenna is a Novel Eight element array configuration in comparison with its performance of a Single element patch antenna. Directivity improvement and Return loss measured for different frequencies and values are compared with a Single element patch antenna. Novel Eight element MIMO array antenna is designed and compared with a Single patch element antenna. In this work, there are two groups (Eight element patch & Single element patch) each consisting of 20 samples with pre-test power or G-power of 80%. The mean Return loss in the Novel eight element MIMO array antenna is -18.39 dB and in the Single element antenna is -15.21 dB. These results showed that the Novel eight element array exhibited a better Directivity improvement (4.483 dB) than the single element antenna (2.261 dB) with the two-tailed significance of (p < 0.05). The Directivity improvement is significantly better for a Novel eight element array antenna when compared with a single element patch antenna.

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