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Computational fluid dynamic and machine learning modeling of nanofluid flow for determination of temperature distribution and models comparison

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Scientific Reports 2025年第1期15卷 1-9页

作者： Altalbawy, Farag M. A. Alkhayyat, Ahmad Jhala, Ramdevsinh Yadav, Anupam Ramachandran, T. Shankhyan, Aman Karthikeyan, A. Thatoi, Dhirendra Nath Sinitsin, Vladimir Vladimirovich Department of Chemistry University College of Duba University of Tabuk Tabuk Saudi Arabia Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Babylon Babylon Iraq Marwadi University Research Center Department of Mechanical Engineering Faculty of Engineering & Technology Marwadi University Gujarat Rajkot 360003 India Department of Computer Engineering and Application GLA University Mathura 281406 India Department of Mechanical Engineering School of Engineering and Technology JAIN (Deemed to be University) Karnataka Bangalore India Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India Department of Mechanical Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai India Department of Mechanical Engineering Siksha ’O’ Anusandhan (Deemed to be University) Odisha Bhubaneswar 751030 India Research Laboratory of Technical Self-Diagnostics and Self-Control of Devices and Systems Department of Scientific and Innovative Activities Research Institute of Mechanical Engineering South Ural State University Lenin prospect 76 Chelyabinsk 454080 Russian Federation

This paper introduces an approach to temperature prediction by employing three distinct machine learning models: K-Nearest Neighbors (KNN), Gaussian Process Regression (GPR), and Multi-layer Perceptron (MLP) which are integrated into Computational Fluid Dynamics (CFD). The dataset consists of inputs, represented by the features x and y, and the corresponding output, which is the temperature. The case study is fluid flow of nanofluid through a pipe and the nanofluid contains CuO particles. To enhance model efficacy, the Political Optimizer (PO) algorithm was utilized for fine-tuning purposes. The findings substantiate the capability of the optimized frameworks in delivering precise temperature estimations. GPR yielded a notable R² value of 0.998, reflecting a strong concordance between the estimated and actual measurements. In a comparable manner, the KNN approach exhibited outstanding predictive performance, also attaining an R² value of 0.998. MLP, although slightly lower in performance compared to the other models, still proves to be a reliable predictor of temperature values, with an R-squared score of 0.984. Overall, the combination of the PO algorithm and the machine learning models showcases promising results in temperature prediction. The study’s findings offer valuable insights for decision-making processes in a variety of domains that rely on accurate temperature forecasting. © The Author(s) 2025.

关键词： Gaussian process regression Heat transfer K-Nearest neighbors Multi-layer perceptron Nanofluid

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Correction for energy level transition under the constraint of elite rate in multiscale quantum harmonic oscillator algorithm 4

Correction for energy level transition under the constraint ...

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4th IEEE International Conference on computer and Communications, ICCC 2018

作者： Mu, Lei Wang, Peng Li, Bo Xin, Gang School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Key Laboratory for Computer Systems of State Ethnic Affairs Commission Southwest Minzu University Chengdu China Chengdu Institute of Computer Application Chinese Academy of Sciences Chengdu China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781538683392

Multiscale quantum harmonic oscillator algorithm (MQHOA) is an optimization algorithm based on quantum behavior. It transforms the function optimization into solving the ground state wave function through the transformation of SchrÖdinger equation and utilizes the processes of energy level stabilization, energy level transition and scale adjustment to search the optimal solution of the objective function. Original MQHOA algorithm accelerates the search process by replacing the worst solution with the mean value in the process of energy level transition. This operation may reduce diversity and lead to prematurity. In order to improve the population diversity of the probes, special elite strategy and random replacement mechanism are used to improve the search efficiency. Experiments on benchmark test functions show that the improved algorithm is more effective than the original algorithm and other common algorithms. © 2018 IEEE.

关键词： Ground state

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Accurate machine-learning based modeling of apparent viscosity of waxy oils mixed with polymeric wax inhibitors

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Flow Measurement and Instrumentation 2025年

作者： Hongyan Lei Muntadher Abed Hussein Tarak Vora Anupam Yadav Asha Rajiv Aman Shankhyan Sachin Jaidka Mehul Manu Issa Mohammed Kadhim Zainab Jamal Hamoodah Fadhil Faez Mehrdad Mottaghi College of Computer and Electrical Engineering Hunan University of Arts and Science Changde hunan 415000 China Department of sciecnes Al-Manara College for Medical Sciences (Maysan) Iraq Marwadi University Research Center Department of Civil Engineering Faculty of Engineering & Technology Marwadi University Rajkot-360003 Gujarat India Department of Computer engineering and Application GLA University Mathura-281406 India Department of Physics & Electronics School of Sciences JAIN (Deemed to be University) Bangalore Karnataka India Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura 140401 Punjab India Department of Physics Department of Applied Sciences Chandigarh Engineering College Chandigarh Group of Colleges-Jhanjeri Mohali 140307 Punjab India Department of Allied Science (Physics) Graphic Era Hill University Bhimtal India Graphic Era Deemed to be University Dehradun Uttarakhand-248002 India Department of Medical Laboratories Technology Al-Nisour University College Nisour Seq. Karkh Baghdad Iraq Mazaya University college An Nasiriyah Dhi Qar Iraq Department of Dentistry College of Dentistry The Islamic University Najaf Iraq Department of medical analysis Medical laboratory technique college the Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of medical analysis Medical laboratory technique college the Islamic University of Babylon Babylon Iraq Faculty of chemistry Kabul University Kabul Afghanistan

While producing and then transporting waxy oils through pipeline network systems, wax precipitation commonly leads to issues such as wax build-up, gel creation, and decreased flow, negatively affecting pipeline efficiency. To address these problems, the petroleum industry frequently uses polymeric flow improvers to enhance the rheological properties, thereby increasing the mobility and flowability of oils. Experimental methods to measure oil viscosity are both expensive and time-consuming; hence, having reliable data driven frameworks are necessary in this regard. This work presents the use of accurate artificial intelligence methods including K-nearest neighbors, convolutional neural network, multi-layer perceptron artificial neural network, decision tree, support vector machine, adaptive boosting, random forest and ensemble learning to create smart methods for calculating the apparent viscosity of waxy crude oils that are doped with polymeric additives by actual databank sourced from prior studies. A databank containing 526 datapoints was used to both prepare and validate these machine learning algorithms. The predicted results were then evaluated against actual lab data using both graphical and statistical methods. The outcomes showed a strong agreement between the determined and real values for the constructed models. These developed models offer a way of accurate estimation capability bypassing the need for costly, labor-intensive, and time-consuming experimental procedures.

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A privacy-preserving traffic monitoring scheme via vehicular crowdsourcing

arXiv

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arXiv 2019年

作者： Zhang, Chuan Zhu, Liehuang Xu, Chang Du, Xiaojiang Guizani, Mohsen Beijing Eng. Res. Center of Massive Language Information Processing and Cloud Computing Application School of Computer Science and Technology Beijing Institute of Technology Beijing China Department of Computer and Information Sciences Temple University Philadelphia United States College of Engineering Qatar University

The explosive growth of vehicle amount has given rise to a series of traffic problems, such as traffic congestion, road safety, and fuel waste. Collecting vehicles' speed information is an effective way to monitor the traffic condition and avoid vehicles being congested, which however may bring threats to vehicles' location and trajectory privacy. Motivated by the fact that traffic monitoring does not need to know each individual vehicle's speed and the average speed would be sufficient, we propose a privacy-preserving traffic monitoring (PPTM) scheme to aggregate vehicles' speeds at different locations. In PPTM, the roadside unit (RSU) collects vehicles' speed information at multiple road segments, and further cooperates with a service provider to calculate the average speed information for every road segment. To preserve vehicles' privacy, both homomorphic Paillier cryptosystem and super-increasing sequence are adopted. A comprehensive security analysis indicates that the proposed PPTM can preserve vehicles' identities, speeds, locations, and trajectories privacy from being disclosed. In addition, extensive simulations are conducted to validate the effectiveness and efficiency of the proposed PPTM scheme. Copyright © 2019, The Authors. All rights reserved.

关键词： Speed

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Image reconstruction with low-rankness and self-consistency of k-space data in parallel MRI

arXiv

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arXiv 2019年

作者： Zhang, Xinlin Guo, Di Huang, Yiman Chen, Ying Wang, Liansheng Huang, Feng Qu, Xiaobo Department of Electronic Science Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance School of Electronic Science and Engineering National Model Microelectronics College Xiamen University Xiamen361005 China School of Computer and Information Engineering Fujian Provincial University Key Laboratory of Internet of Things Application Technology Xiamen University of Technology Xiamen361024 China Department of Computer Science School of Information Science and Engineering Xiamen University Xiamen361005 China Neusoft Medical System Shanghai200241 China

Parallel magnetic resonance imaging has served as an effective and widely adopted technique for accelerating scans. The advent of sparse sampling offers aggressive acceleration, allowing flexible sampling and better reconstruction. Nevertheless, faithfully reconstructing the image from limited data still poses a challenging task. Recent low-rank reconstruction methods exhibit superiority in providing a high-quality image. However, none of them employ the routinely acquired calibration data for improving image quality in parallel magnetic resonance imaging. In this work, an image reconstruction approach named STDLR-SPIRiT was proposed to explore the simultaneous two-directional low-rankness (STDLR) in the k-space data and to mine the data correlation from multiple receiver coils with the iterative self-consistent parallel imaging reconstruction (SPIRiT). The reconstruction problem was then solved with a singular value decomposition-free numerical algorithm. Experimental results of phantom and brain imaging data show that the proposed method outperforms the state-of-the-art methods in terms of suppressing artifacts and achieving the lowest error. Moreover, the proposed method exhibits robust reconstruction even when the auto-calibration signals are limited in parallel imaging. Overall the proposed method can be exploited to achieve better image quality for accelerated parallel magnetic resonance imaging. Copyright © 2019, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Person re-identification in aerial imagery

arXiv

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arXiv 2019年

作者： Zhang, Shizhou Zhang, Qi Yang, Yifei Wei, Xing Wang, Peng Jiao, Bingliang Zhang, Yanning National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an710072 China Faculty of Electronic and Information Engineering Xi’an Jiaotong University Xi’an710049 China

Nowadays, with the rapid development of consumer Unmanned Aerial Vehicles (UAVs), visual surveillance by utilizing the UAV platform has been very attractive. Most of the research works for UAV captured visual data are mainly focused on the tasks of object detection and tracking. However, limited attention has been paid to the task of person Re-identification (ReID) which has been widely studied in ordinary surveillance cameras with fixed emplacements. In this paper, to facilitate the research of person ReID in aerial imagery, we collect a large scale airborne person ReID dataset named as Person ReID for Aerial Imagery (PRAI-1581), which consists of 39,461 images of 1581 person identities. The images of the dataset are shot by two DJI consumer UAVs flying at an altitude ranging from 20 to 60 meters above the ground, which covers most of the real UAV surveillance scenarios. In addition, we propose to utilize subspace pooling of convolution feature maps to represent the input person images. Our method can learn a discriminative and compact feature representation for ReID in aerial imagery and can be trained in an end-to-end fashion efficiently. We conduct extensive experiments on the proposed dataset and the experimental results demonstrate that re-identify persons in aerial imagery is a challenging problem, where our method performs favorably against state of the arts. Our dataset can be accessed via https://***/stormyoung/PRAI-1581. Copyright © 2019, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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A Dual Circular-Polarized Antenna for mmWave Wireless Communications

A Dual Circular-Polarized Antenna for mmWave Wireless Commun...

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European Conference on Antennas and Propagation

作者： Chao Shu Junbo Wang Shaoqing Hu Junxiao Shen Yuan Yao Xiaodong Chen School of Electronic Engineering and Computer Science Queen Mary University of London London UK BUPT-QMUL EM Theory & Application International Research Lab Beijing University of Posts and Telecommunications Beijing China Engineering Department University of Cambridge UK

This paper presents our study on an antenna with dual circular polarization operating in W-Band. The antenna is capable of transmitting and receiving two orthogonal circular polarized signals (Left Hand Circular Polarization and Right Hand Circular Polarization) simultaneously to achieve Full-Duplex hence twofold spectrum efficiency in mmWave wireless communication systems. It mainly consists of a stepped septum polarizer and a profiled smooth-wall horn. The fabricated antenna shows 21% relative bandwidth from 76.8 GHz to 95 GHz with AR < 1.9 dB, the return loss is better than 15 dB and isolation> 20 dB for both LHCP and RHCP. It is also quite suitable to be used as a feed for a reflector antenna when high gain is also required besides high spectrum efficiency.

关键词： Antenna measurements Polarization Wireless communication Bandwidth Horn antennas Electromagnetic waveguides

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IoU loss for 2D/3D object detection

arXiv

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arXiv 2019年

作者： Zhou, Dingfu Fang, Jin Song, Xibin Guan, Chenye Yin, Junbo Dai, Yuchao Yang, Ruigang Baidu Research National Engineering Laboratory of Deep Learning Technology and Application China Beijing Lab of Intelligent Information Technology School of Computer Science Beijing Institute of Technology China Northwestern Polytechnical University Xi'an China

In 2D/3D object detection task, Intersection-over-Union (IoU) has been widely employed as an evaluation metric to evaluate the performance of different detectors in the testing stage. However, during the training stage, the common distance loss (e.g., L1 or L2) is often adopted as the loss function to minimize the discrepency between the predicted and ground truth Bounding Box (Bbox). To eliminate the performance gap between training and testing, the IoU loss has been introduced for 2D object detection in [1] and [2]. Unfortunately, all these approaches only work for axis-aligned 2D Bboxes, which cannot be applied for more general object detection task with rotated Bboxes. To resolve this issue, we investigate the IoU computation for two rotated Bboxes first and then implement a unified framework, IoU loss layer for both 2D and 3D object detection tasks. By integrating the implemented IoU loss into several state-of-the-art 3D object detectors, consistent improvements have been achieved for both bird-eye-view 2D detection and point cloud 3D detection on the public KITTI [3] benchmark. Copyright © 2019, The Authors. All rights reserved.

关键词： Object recognition

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Deep generative autoencoder for low-dimensional embeding extraction from single-cell RNAseq data

Deep generative autoencoder for low-dimensional embeding ext...

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IEEE International Conference on Bioinformatics and Biomedicine

作者： Shiquan Sun Yang Liu Xuequn Shang National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Northwestern Polytechnical UniversityXi'an ShaanxiPeople's Republic of China710129 School of Computer Science Northwestern Polytechnical Universit

ISBN: (纸本)9781728118680

Single-cell RNA sequencing (scRNAseq) can reveal biological diversity at the cellular level that are unexplored by bulk RNA sequencing (RNAseq), but they suffer from the excessive zero expression counts and the limitation of the scalability in practice. Here, we propose a non-linear generative autoencoder based method, scSVA, relying on an integration of variational autoencoder and dropout imputations. Specifically, scSVA automatically identifies the dropouts and recovery these values only to avoid introducing new biases. Then, scSVA utilizes stochastic optimization and deep neural network to extract the low-dimensional embedding from gene expression levels. We illustrate the benefits of scSVA through in-depth real analyses of six published scRNAseq data sets. scSVA is up to 1.3 times more powerful cell clustering accuracy than existing approaches. The high power of scSVA allows us to identify new cell types that reveal new biology from scRNAseq data that otherwise cannot be revealed by existing approaches.

关键词： bioinformatics cellular biophysics data analysis genetics genomics molecular biophysics neural nets pattern clustering RNA stochastic processes cellular biophysics molecular biology Neural network Markov chain Cluster Analysis Bioinformatics Genomics Data analysis Genetics RNA Stochastic Processes SINGLE CELL Analyses, Sequence RNA Sequence Statistical analysis

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SEMI-SUPERVISED RECURRENT COMPLEX-VALUED CONVOLUTION NEURAL NETWORK FOR POLSAR IMAGE CLASSIFICATION

SEMI-SUPERVISED RECURRENT COMPLEX-VALUED CONVOLUTION NEURAL ...

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IEEE International Geoscience and Remote Sensing Symposium

作者： Feng Zhao Gaini Ma Wen Xie Hanqiang Liu Ministry of Public Security Key Laboratory of Electronic Information Application Technology for Scene Investigation Xi'an China School of Communications and Information Engineering Xi'an University of Posts and Telecommunication Xi'an China School of Computer Science Shaanxi Normal University Xi'an China

This paper presents a novel semi-supervised terrain classification method of polarimetric synthetic aperture radar (PolSAR) image based on complex-valued convolution neural network (CV-CNN). Our proposed method only needs a small number of labeled samples to achieve good classification results. First, a Wishart classifier is used to find highly reliable samples in PolSAR data. Then, a new semi-supervised deep recurrent CV-CNN (RCV-CNN) classification model has been proposed to improve PolSAR image classification accuracy and effectively solve network overfitting. Finally, a real PolSAR dataset is used to verify the effectiveness of our algorithm. Compared with the other three state-of-the-art methods, the proposed one show improvements in accuracy and better consistency.

关键词： Training Reliability Classification algorithms Image classification Convolutional neural networks Data preprocessing

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