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Influence of Training Times and Sample Size on Results in Visual Try-On with GAN

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Journal of Physics: Conference Series 2021年第1期1903卷

作者： Wenqing Jiang Ruiyu Qi Juntao Zheng Computer Science and Technology Beijing Normal University-Hong Kong Baptist University United International College Zhuhai Guangdong 519087 China Chemical and Biomolecular Engineering Lehigh University Bethlehem PA 18015 U.S. Internet of things Guangdong Ocean University Zhanjiang Guangdong 524088 China

With online shopping becoming more and more trendy these days, Virtual Try-On Approach is proposed. It is a method of Generative Adversarial Network (GAN) composes of two models: Geometry Matching Module (GMM) and Try-On Module (TOM). It is proposed to solve the inconvenience of customers for not being able to see the fitting effect before receiving the goods. This paper is about finding the relationship between dataset and the effect of the experiment in the GAN experiment. After implementing the models and gaining the result images, we do some observations on the GMM one and try to analyze it and improve the effect. Comparison is made on different aspects: training times of the model and sample size of the training input. Similar trends are shown on the decrease of loss value when the training times or sample size rises. However, the visual effect is not necessarily improved all the time. Compared with the times it has been trained, the improvement from training times is far less than that from the sample size. Finally, we draw the conclusion that the effect is gradually improved as the training times or sample size increases at the beginning. Nevertheless, the improvement cannot be recognized evidently when the training times and sample size have grown large enough. Additionally, the sample size seems to be a more significant factor in the improvement of the visual effect than the training times.

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Operational Research: Methods and Applications

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arXiv 2023年

作者： Petropoulos, Fotios Laporte, Gilbert Aktas, Emel Alumur, Sibel A. Archetti, Claudia Ayhan, Hayriye Battarra, Maria Bennell, Julia A. Bourjolly, Jean-Marie Boylan, John E. Breton, Michèle Canca, David Charlin, Laurent Chen, Bo Cicek, Cihan Tugrul Cox, Louis Anthony Currie, Christine S.M. Demeulemeester, Erik Ding, Li Disney, Stephen M. Ehrgott, Matthias Eppler, Martin J. Erdogan, Güneş Fortz, Bernard Franco, L. Alberto Frische, Jens Greco, Salvatore Gregory, Amanda J. Hämäläinen, Raimo P. Herroelen, Willy Hewitt, Mike Holmström, Jan Hooker, John N. Işık, Tugçe Johnes, Jill Kara, Bahar Y. Karsu, Özlem Kent, Katherine Köhler, Charlotte Kunc, Martin Kuo, Yong-Hong Letchford, Adam N. Leung, Janny Li, Dong Li, Haitao Lienert, Judit Ljubić, Ivana Lodi, Andrea Lozano, Sebastián Lurkin, Virginie Martello, Silvano McHale, Ian G. Midgley, Gerald Morecroft, John D.W. Mutha, Akshay Oguz, Ceyda Petrovic, Sanja Pferschy, Ulrich Psaraftis, Harilaos N. Rose, Sam Saarinen, Lauri Salhi, Said Song, Jing-Sheng Sotiros, Dimitrios Stecke, Kathryn E. Strauss, Arne K. Tarhan, Istenç Thielen, Clemens Toth, Paolo Van Woensel, Tom Vanden Berghe, Greet Vasilakis, Christos Vaze, Vikrant Vigo, Daniele Virtanen, Kai Wang, Xun Weron, Rafal White, Leroy Yearworth, Mike Yıldırım, E. Alper Zaccour, Georges Zhao, Xuying School of Management University of Bath Bath United Kingdom Makridakis Open Forecasting Center University of Nicosia Nicosia Cyprus Department of Decision Sciences HEC Montréal Montreal Canada Molde University College Molde Norway Cranfield School of Management Cranfield University Cranfield United Kingdom Department of Management Sciences University of Waterloo Waterloo Canada ESSEC Business School in Paris Department of Information Systems Decision Sciences and Statistics Cergy France H. Milton Stewart School of Industrial and Systems Engineering Georgia Institute of Technology Atlanta United States Centre for Decision Research Leeds University Business School University of Leeds Leeds United Kingdom Université du Québec à Montréal Montreal Canada Centre for Marketing Analytics and Forecasting Lancaster University Management School Lancaster University Lancaster United Kingdom School of Engineering Department of Industrial Engineering and Management Science I University of Seville Seville Spain Mila-Quebec AI Institute Montreal Canada Warwick Business School University of Warwick Coventry United Kingdom Department of Industrial Engineering Atilim University Ankara Turkey Department of Business Analytics University of Colorado School of Business Denver United States Cox Associates Denver United States Mathematical Sciences University of Southampton Southampton United Kingdom Faculty of Economics and Business Research Center for Operations Management KU Leuven Leuven Belgium Durham University Business School Durham University Durham United Kingdom Center for Simulation Analytics and Modelling University of Exeter Business School Exeter United Kingdom Department of Management Science Lancaster University Management School Lancaster University Lancaster United Kingdom University of St. Gallen St. Gallen Switzerland HEC - Management School of the University of Liège Liège Belgium Département d'Informatique Université Libre de Bruxelles Bruss

Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order. The authors dedicate this paper to the 2023 Turkey/Syria earthquake victims. We sincerely hope that advances in OR will play a role towards minimising the pain and suffering caused by this and future catastrophes. © 2023, CC BY.

关键词： Decision making

Traffic control system for vehicles on Indian roads using raspberry Pi

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IOP Conference Series: Materials science and engineering 2020年第3期981卷

作者： Bura Vijay Kumar Seena Naik Korra N Swathi D. Kothandaraman Nagender Yamsani Yerrolla Chanti Member Center for Embedded Systems & Internet of Things Department of Computer Science and Engineering S R University Warangal Telangana Department of Computer Science and Engineering S R University Warangal Telangana Sumathi Reddy Institute of Technology for Women Warangal Telangana India Member Center for AI & Deep Learning Department of Computer Science and Engineering S R Engineering College Warangal Telangana India Department of Computer Science and Engineering S R Engineering College Warangal Telangana India

In India at the roads the vehicles were growing because of the increase of population and controlling of traffic is one of the tough duties for the individuals who control the traffic. The everyday traffic congestion at crucial junctions becomes more issues for the emergency automobiles and ought to wait for until inexperienced signal. This results the growth in pollution ranges and wastage of time, pollutants stages may boom to a huge scale. Previously the traffic control strategies used like magnetic loop detectors, induction loop detectors are buried on the street aspect provide the constrained visitors information and necessitate separate tracking structures for site traffic counting and for traffic surveillance. Right here the undertaking proposes to enforce a synthetic density traffic manipulate gadget using photo processing and Raspberry pi.

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

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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

On RNN Models for Solving Dynamic System of Linear Equations

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On RNN Models for Solving Dynamic System of Linear Equations

International Conference on Intelligent Control and Information Processing (ICICIP)

作者： Huiyan Lu Ruiqi Liu Xiujuan Du Haiqi Liu Mei Lin Long Jin Jiliang Zhang Special Environment Key Laboratory of Sichuan Province Southwest University of Science and Technology Mianyang China The Fifth Electronics Research Institute of MIIT Guangzhou China The Provincial key Laboratory of the Internet of Things Qinghai Normal University Xi'ning China School of Computer Science and Engineering South China University of Technology Guangzhou China School of Information Science and Engineering Lanzhou University Lanzhou China

ISBN: (数字)9781728100159

ISBN: (纸本)9781728100166

Neural networks have a wide range of applications in dealing with various online computing problems. This paper mainly retrospects one of the latest recurrent neural network (RNN) models and supplies summarizes on it. Firstly, formulations on the RNN model for dealing with the dynamic underdetermined system of linear equations with double bound constraints on state variables and residual errors are presented. Secondly, simple structures of the RNN model, that is, the neuron-connection architecture of RNN model for handling with the perturbed dynamic underdetermined linear system, as well as the RNN model and the unfolding in time of the computation involved in its forward computation are analyzed. In addition, the whole flowchart on the presented method for establishing the RNN model is also given. Then, experiments on executing the tasks of the UR5 robot when the end-effector tracks a “four-leaf clover” path and a “tricuspid valve” path synthesized by the RNN model are conducted, which show the superiority and accuracy of the presented RNN model.

关键词： Mathematical model Computational modeling Analytical models computer architecture Linear systems Robots Task analysis

Multi-view Vector-valued Manifold Regularization for Multi-label Image Classification

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arXiv 2019年

作者： Luo, Yong Tao, Dacheng Xu, Chang Xu, Chao Liu, Hong Wen, Yonggang School of Electronics Engineering and Computer Science Peking University Beijing100871 China Centre for Quantum Computation and Intelligent Systems University of Technology Sydney Jones Street Ultimo SydneyNSW2007 Australia Engineering Lab on Intelligent Perception for Internet of Things Shenzhen Graduate School Peking University China Division of Networks and Distributed Systems School of Computer Engineering Nanyang Technological University Singapore Singapore

—In computer vision, image datasets used for classification are naturally associated with multiple labels and comprised of multiple views, because each image may contain several objects (e.g. pedestrian, bicycle and tree) and is properly characterized by multiple visual features (e.g. color, texture and shape). Currently available tools ignore either the label relationship or the view complementary. Motivated by the success of the vector-valued function that constructs matrix-valued kernels to explore the multi-label structure in the output space, we introduce multi-view vector-valued manifold regularization (MV3MR) to integrate multiple features. MV3MR exploits the complementary property of different features and discovers the intrinsic local geometry of the compact support shared by different features under the theme of manifold regularization. We conducted extensive experiments on two challenging, but popular datasets, PASCAL VOC’ 07 (VOC) and MIR Flickr (MIR), and validated the effectiveness of the proposed MV3MR for image classification. Copyright © 2019, The Authors. All rights reserved.

关键词： Image classification

HYPERSPECTRAL PANSHARPENING VIA MULTITASK JOINT SPARSE REPRESENTATION

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HYPERSPECTRAL PANSHARPENING VIA MULTITASK JOINT SPARSE REPRE...

IEEE International Geoscience and Remote Sensing Symposium

作者： Jianjun Liu Zebin Wu Zhiyong Xiao Jinlong Yang School of Internet of Things Engineering Jiangnan University Wuxi China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

In this paper, a high spatial resolution (HR) hyperspectral image is inferred from a low spatial resolution (LR) hyperspectral image and a HR panchromatic image by taking advantage of the sparse representation pansharpening (SRP) method. Different from the conventional SRP or joint SRP (JSRP) method, this paper proposes a multitask JSRP method for hyperspectral pansharpening, in order to improve the generalization performance of the model. First, multiple HR/LR dictionary pairs are generated by partitioning the multiple features of the panchromatic image and their corresponding downsampled LR versions into patches. Second, the patch-level sparse representation coefficients of the multiple LR hyperspectral image features are jointly estimated under the multiple LR dictionaries. Finally, the estimated sparse representation coefficients are utilized to reconstruct the HR patches under the original HR dictionary, and the desired HR hyperspectral image is obtained by aggregating the HR patches. Experimental results conducted on two hyperspectral scenes demonstrate the effectiveness of the proposed method.

关键词： Hyperspectral imaging Dictionaries Spatial resolution Feature extraction Image reconstruction

A convergence proof of projected fast iterative soft-thresholding algorithm for parallel magnetic resonance imaging

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arXiv 2019年

作者： Zhang, Xinlin Lu, Hengfa Guo, Di Bao, Lijun 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 Neusoft Medical System Shanghai200241 China

The boom of non-uniform sampling and compressed sensing techniques dramatically alleviates the prolonged data acquisition problem of magnetic resonance imaging. Sparse reconstruction, thanks to its fast computation and promising performance, has attracted researchers to put numerous efforts on it and has been adopted in commercial scanners. Algorithms for solving the sparse reconstruction models play an essential role in sparse reconstruction. Being a simple and efficient algorithm for sparse reconstruction, pFISTA has been successfully extended to parallel imaging, however, its convergence criterion is still an open question, confusing users on the setting of the parameter which assures the convergence of the algorithm. In this work, we prove the convergence of the parallel imaging version pFISTA. Specifically, the convergences of two well-known parallel imaging reconstruction models, SENSE and SPIRiT, solved by pFISTA are proved. Experiments on brain images demonstrate the validity of the convergence criterion. The convergence criterion proofed in this work can help users quickly obtain the satisfy parameter that admits faithful results and fast convergence speeds. Copyright © 2019, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

Enhancing wircless channel authentication in industrial control: Attack-resistant CSI-based PUF

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Ad Hoc Networks 2025年 178卷

作者： Song, Yubo Liu, Wenchang Zhu, Hongyu Gong, Yi Li, Yang Huang, Jiyuan Deng, Yazhi Key Laboratory of Computer Network Technology of Jiangsu Province School of Cyber Science and Engineering Southeast University No. 2 Southeast University Road Jiangning District Jiangsu Nanjing211151 China Purple Mountain Laboratories No. 9 Mozhou east road Jiangning District Jiangsu Nanjing211151 China Hunan Key Laboratory for Internet of Things in Electricity No. 398 Xin Shaodong Road Tianxin District Hunan Changsha410002 China State Grid Hunan Electric Power Co. Ltd. No. 398 Xin Shaodong Road Tianxin District Hunan Changsha410002 China State Grid Hunan Electric Power Company Limited Information and Communication Company No. 398 Xin Shaodong Road Tianxin District Hunan Changsha410002 China

The extensive deployment of wireless networks in industrial settings has led to a surge in wireless-connected devices, presenting formidable security challenges. Traditional encryption approaches, relying on cryptographic keys without binding to physical hardware details, are highly susceptible to cloning attacks when the keys are compromised. This paper presents a novel solution: a Channel State Information-based Physical Unclonable Function (CSI-PUF) for wireless devices. By leveraging CSI, it generates a unique device identity. A meticulously designed fuzzy extractor algorithm is incorporated. In the enrollment phase, random numbers are generated and encoded with BCH error correction codes. During the reconstruction phase, errors in the CSI-PUF output bit sequence are transferred to the BCH-encoded random numbers. This allows for error elimination through decoding, safeguarding the confidentiality of CSI. A prototype WiFi terminal authentication system based on this CSI-PUF framework is developed and implemented. Experimental evaluations in an interference-free industrial control environment reveal that the system attains an average authentication success rate exceeding 95%. This significantly bolsters the security of WiFi-enabled devices, indicating that the CSI-PUF can effectively enhance the authentication security of wireless devices in industrial scenarios. © 2025 Elsevier B.V.

关键词： Authentication