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HOG-VGG:VGG Network with HOG Feature Fusion for High-Precision PolSAR Terrain Classification

Journal of Harbin Institute of Technology(New Series) 2024年第5期31卷 1-15页

作者： Jiewen Li Zhicheng Zhao Yanlan Wu Jiaqiu Ai Jun Shi School of Computer Science and Information Engineering Hefei University of TechnologyHefei 230009China Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui UniversityHefei 230601China School of Artificial Intelligence Anhui UniversityHefei 230601China Intelligent Interconnected Systems Laboratory of Anhui Province(Hefei University of Technology) Hefei 230009China School of Software Hefei University of TechnologyHefei 230009China

This article proposes a VGG network with histogram of oriented gradient(HOG) feature fusion(HOG-VGG) for polarization synthetic aperture radar(PolSAR) image terrain ***-Net has a strong ability of deep feature extraction,which can fully extract the global deep features of different terrains in PolSAR images,so it is widely used in PolSAR terrain ***,VGG-Net ignores the local edge & shape features,resulting in incomplete feature representation of the PolSAR terrains,as a consequence,the terrain classification accuracy is not *** fact,edge and shape features play an important role in PolSAR terrain *** solve this problem,a new VGG network with HOG feature fusion was specifically proposed for high-precision PolSAR terrain ***-VGG extracts both the global deep semantic features and the local edge & shape features of the PolSAR terrains,so the terrain feature representation completeness is greatly ***,HOG-VGG optimally fuses the global deep features and the local edge & shape features to achieve the best classification *** superiority of HOG-VGG is verified on the Flevoland,San Francisco and Oberpfaffenhofen *** show that the proposed HOG-VGG achieves much better PolSAR terrain classification performance,with overall accuracies of 97.54%,94.63%,and 96.07%,respectively.

关键词： PolSAR terrain classification high⁃precision HOG⁃VGG feature representation completeness elevation multi⁃level feature fusion

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Medical Treatment Process Modeling Based on Process Mining and Treatment Patterns

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China Communications 2021年第12期18卷 332-349页

作者： Liqin Yang Guosheng Kang Liang Zhang School of Computer Science Fudan UniversityShanghai 200438China Shanghai Key Laboratory of Data Science Shanghai 200438China Institute of Intelligent Electronics and Systems Shanghai 200438China Computer Teaching and Research Section Shanghai University of Traditional Chinese MedicineShanghai 201203China School of Computer Science and Engineering Hunan University of Science and TechnologyXiangtan 411201China

***-periments on a synthetic log of the non-secondary hy-pertension MTP and empirical findings demonstrate the effectiveness of our *** results show that the process mining in our approach framework can automatically generate more accurate MTP mod-els,and the subprocess models based on treatment pat-terns make the models easy to understand.

关键词： business process modeling medical treat-ment processes treatment patterns clinical practice guidelines

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A Short Survey on Multimodal Data Fusion in Image Classification

A Short Survey on Multimodal Data Fusion in Image Classifica...

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Global Aeronautical engineering and Satellite Technology (GAST), International Conference on

作者： Toufik Datsi Khalid Aznag Brahim Ait BenAli Karim Karbout Ahmed El Oirrak El Kharrachi Khayya Faculty of Sciences Semlalia Computer Systems Engineering Laboratory Cadi Ayyad University Marrakesh Morocco LTI Laboratory ENSA School-University Chouaib Doukkali EL Jadida Morocco Faculty of Sciences Semlalia Computer Science Engineering Laboratory Cadi Ayyad University Marrakesh Morocco

ISBN: (数字)9798350371598

ISBN: (纸本)9798350371604

Advancements in multimodal learning have experienced rapid growth over the past decade, particularly within various domains, with a significant emphasis on developments in computer vision. Multimodal data fusion has become increasingly prominent in the realm of image classification, where the integration of diverse data sources enhances the overall understanding and performance of classification models. This survey delves into the recent strides made in multimodal learning over the past decade, particularly within the field of image classification. Additionally, the paper undertakes a comparative study, critically evaluating the effectiveness and performance of different multimodal fusion approaches. The aim is to provide a comprehensive overview of the current state-of-the-art in multimodal data fusion for image classification and to identify key trends, challenges, and opportunities in this evolving field.

关键词： Surveys Adaptation models Satellites Navigation Soft sensors Data integration Data models

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On Space-Filling Input Design for Nonlinear Dynamic Model Learning: A Gaussian Process Approach

arXiv

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arXiv 2025年

作者： Liu, Yuhan Kiss, Máté Tóth, Roland Schoukens, Maarten Control Systems Group Department of Electrical Engineering Eindhoven University of Technology Eindhoven5600 MB Netherlands Systems and Control Laboratory HUN-REN Institute for Computer Science and Control Budapest1111 Hungary

While optimal input design for linear systems has been well-established, no systematic approach exists for nonlinear systems, where robustness to extrapolation/interpolation errors is prioritized over minimizing estimated parameter variance. To address this issue, we develop a novel identification-oriented space-filling input design strategy for efficient covering of a well-defined region of interest. By placing a Gaussian Process (GP) prior on the joint input-state space, the proposed strategy leverages the GP posterior variance to construct a space-filling promoting input design cost function from the Bayesian point of view. This accordingly enables maximization of the coverage in the region of interest, thereby facilitating the generation of informative datasets. Furthermore, we theoretically prove that minimization of the cost function indicates the space-filling behavior. The effectiveness of the proposed strategy is demonstrated on both an academic example and a mass-spring-damper system, highlighting its potential practical impact on efficient exploration in identification scenarios for nonlinear dynamical systems. © 2025, CC BY-NC-SA.

关键词： Cost functions

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Data-Driven Analysis of Skin Cancer Classification with Convolutional Neural Networks for E-Health Applications

Data-Driven Analysis of Skin Cancer Classification with Conv...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Ahmed, Imran Ahmad, Misbah Chehri, Abdellah Jeon, Gwanggil Anglia Ruskin University School of Computing and Information Science Cambridge United Kingdom Hartpury University Animal and Agriculture Department Gloucester United Kingdom University of the West of England Centre for Machine Vision Bristol Robotics Laboratory Bristol United Kingdom Department of Mathematics and Computer Science Canada Incheon National University Department of Embedded Systems Engineering Incheon Korea Republic of

ISBN: (纸本)9798350351255

This study explores the effectiveness of Convolutional Neural Networks (CNNs) in automatically classifying skin cancer for e-health applications. The trained model showcases impressive performance by leveraging the HAM10000 dataset, which includes a wide range of skin lesion images from seven different classes. The parameters and architecture of the CNN model are presented in a systematic manner, providing valuable insights into the reasoning behind its design. The model is optimized using the Adam optimizer and annealing techniques to ensure efficient convergence. The model's performance is assessed on validation and test datasets, showcasing an accuracy of 78.55% and 76.49%, respectively, for skin cancer classification. This study highlights the significant potential of CNN as a powerful tool for automating the diagnosis of skin cancer, which is in line with the growing trend of using deep learning for medical image analysis. © 2024 IEEE.

关键词： Electronic health record

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Energy-Efficient Operation of Train Platoons in Urban Rail Transit systems: An MIP Approach 43

Energy-Efficient Operation of Train Platoons in Urban Rail T...

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43rd Chinese Control Conference, CCC 2024

作者： Li, Haodong Yin, Jiateng Tang, Tao D'Ariano, Andrea You, Mengxing Beijing Jiaotong University State Key Laboratory of Advanced Rail Autonomous Operation Beijing100044 China Beijing Jiaotong University School of Systems Science Beijing100044 China RomaTre Universily Computer Science and Aeronautical Technologies Engineering Department of Civi Rome0018511 Italy Nanchang Rail Transit Group CO. Ltd Jiangxi330038 China

ISBN: (纸本)9789887581581

Train platooning, which allows multiple train units to be virtually coupled into a platoon with very short following distances, has become an emerging technology in railway industry. Our study investigates the energy-efficient operation of train platoons to reduce the energy consumption of trains while satisfying time-varying passenger demands in practice. We develop a mixed integer programming (MIP) model to optimize the timetable, running speed and coupling strategies of trains. Considering that the small-size train platoons consume much less energy but can serve fewer passengers, our model involves bi-objectives, i.e., to minimize the passenger waiting time and to minimize the overall energy consumption of trains. We also construct a series of model constraints to limit the movement of trains as well as the time-dependent flows of passengers, since a part of passengers during the peak period may have to wait for a longer time due to the capacity limitation of different sizes of trains. Finally, we conduct real-world case studies on the Beijing subway Yizhuang line to verify the effectiveness of our approach. The results indicate that the energy-efficient operation of train platoons can achieve a better trade-off between service quality and system energy consumption. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Energy efficiency

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What limits limits?

引用

National science Review 2021年第1期8卷 11-14页

作者： Yong-Chun Liu Kun Huang Yun-Feng Xiao Lan Yang Cheng-Wei Qiu State Key Laboratory of Low-Dimensional Quantum Physics Department of Physics Frontier Science Center for Quantum Information Tsinghua University Department of Optics and Optical Engineering University of Science and Technology of China State Key Laboratory for Mesoscopic Physics and Frontiers Science Center for Nano-optoelectronics School of Physics Peking University Department of Electrical and Systems Engineering Washington University Department of Electrical and Computer Engineering National University of Singapore

There are fundamental rules and principles setting the limits of physical systems. It triggers an interesting thought—can we break the limits under specific circumstances? A realistic system can only provide limited functionalities because its performance is physically constrained by some fundamental principles.‘Breaking the limit’, which usually implies that the capability of a system could be enhanced significantly,

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AdaLog: Post-Training Quantization for Vision Transformers with Adaptive Logarithm Quantizer

arXiv

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arXiv 2024年

作者： Wu, Zhuguanyu Chen, Jiaxin Zhong, Hanwen Huang, Di Wang, Yunhong State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China School of Computer Science and Engineering Beihang University Beijing China

Vision Transformer (ViT) has become one of the most prevailing fundamental backbone networks in the computer vision community. Despite the high accuracy, deploying it in real applications raises critical challenges including the high computational cost and inference latency. Recently, the post-training quantization (PTQ) technique has emerged as a promising way to enhance ViT's efficiency. Nevertheless, existing PTQ approaches for ViT suffer from the inflexible quantization on the post-Softmax and post-GELU activations that obey the power-law-like distributions. To address these issues, we propose a novel non-uniform quantizer, dubbed the Adaptive Logarithm AdaLog (AdaLog) quantizer. It optimizes the logarithmic base to accommodate the power-law-like distribution of activations, while simultaneously allowing for hardware-friendly quantization and de-quantization. By employing the bias reparameterization, the AdaLog quantizer is applicable to both the post-Softmax and post-GELU activations. Moreover, we develop an efficient Fast Progressive Combining Search (FPCS) strategy to determine the optimal logarithm base for AdaLog, as well as the scaling factors and zero points for the uniform quantizers. Extensive experimental results on public benchmarks demonstrate the effectiveness of our approach for various ViT-based architectures and vision tasks including classification, object detection, and instance segmentation. Code is available at https://***/GoatWu/AdaLog. Copyright © 2024, The Authors. All rights reserved.

关键词： Chemical activation

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Two-Stage Estimation and Variance Modeling for Latency-Constrained Variational Quantum Algorithms

arXiv

引用

arXiv 2024年

作者： Ha, Yunsoo Shashaani, Sara Menickelly, Matt Department of Industrial and Systems Engineering North Carolina State University United States Mathematics and Computer Science Division Argonne National Laboratory United States

The Quantum Approximate Optimization Algorithm (QAOA) has enjoyed increasing attention in noisy intermediate-scale quantum computing due to its application to combinatorial optimization problems. Because combinatorial optimization problems are NP-hard, QAOA could serve as a potential demonstration of quantum advantage in the future. As a hybrid quantum-classical algorithm, the classical component of QAOA resembles a simulation optimization problem, in which the simulation outcomes are attainable only through the quantum computer. The simulation that derives from QAOA exhibits two unique features that can have a substantial impact on the optimization process: (i) the variance of the stochastic objective values typically decreases in proportion to the optimality gap, and (ii) querying samples from a quantum computer introduces an additional latency overhead. In this paper, we introduce a novel stochastic trust-region method, derived from a derivative-free adaptive sampling trust-region optimization (ASTRO-DF) method, intended to efficiently solve the classical optimization problem in QAOA, by explicitly taking into account the two mentioned characteristics. The key idea behind the proposed algorithm involves constructing two separate local models in each iteration: a model of the objective function, and a model of the variance of the objective function. Exploiting the variance model allows us to both restrict the number of communications with the quantum computer, and also helps navigate the nonconvex objective landscapes typical in the QAOA optimization problems. We numerically demonstrate the superiority of our proposed algorithm using the SimOpt library and Qiskit, when we consider a metric of computational burden that explicitly accounts for communication costs. Copyright © 2024, The Authors. All rights reserved.

关键词： Quantum computers

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An Adaptive Decision-Making Approach for Better Selection of Blockchain Platform for Health Insurance Frauds Detection with Smart Contracts: Development and Performance Evaluation 14

An Adaptive Decision-Making Approach for Better Selection of...

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14th International Conference on Ambient systems, Networks and Technologies Networks, ANT 2023 and The 6th International Conference on Emerging Data and Industry 4.0, EDI40 2023

作者： Kaafarani, Rima Ismail, Leila Zahwe, Oussama ICCS-Lab Computer Science Department American University of Culture and Education Beirut1507 Lebanon Laboratory School of Computing and Information Systems The University of Melbourne Melbourne Australia Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Abu Dhabi United Arab Emirates National Water and Energy Center United Arab Emirates University Abu Dhabi United Arab Emirates

Blockchain technology has piqued the interest of businesses of all types, while consistently improving and adapting to business requirements. Several blockchain platforms have emerged, making it challenging to select a suitable one for a specific type of business. This paper presents a classification of over one hundred blockchain platforms. We develop smart contracts for detecting healthcare insurance frauds using the top two blockchain platforms selected based on our proposed decision-making map approach which selects the top suitable platforms for healthcare insurance frauds detection application. Our classification shows that the largest percentage of platforms can be used for all types of application domains, the second biggest percentage for financial services, and a small number is to develop applications in specific domains. Our decision-making map and performance evaluations reveal that Hyperledger Fabric surpassed Neo in all metrics for detecting healthcare insurance frauds. © 2023 Elsevier B.V.. All rights reserved.

关键词： Smart contract

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