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25th International Conference on Digital Image computing: Techniques and Applications, DICTA 2024

作者： Siddiqui, Mahrukh Iqbal, Shahzaib Alhaqbani, Bandar Alshammari, Bandar Khan, Tariq Razzak, Imran Technology Control Company Tcc Research and Development Labs Riyadh Saudi Arabia Department of Computing Islamabad Pakistan School of Computer Science and Eng. University of New South Wales Sydney Australia

ISBN: (纸本)9798350379037

Compared to contact fingerprint images, contactless fingerprint images exhibit four distinct characteristics: (1) they contain less noise;(2) they have fewer discontinuities in ridge patterns;(3) the ridge-valley pattern is less distinct;and (4) they pose an interoperability problem, as they lack the elastic deformation caused by pressing the finger against the capture device. These properties present significant challenges for the enhancement of contactless fingerprint images. In this study, we propose a novel contactless fingerprint identification solution that enhances the accuracy of minutiae detection through improved frequency estimation and a new region-quality-based minutia extraction algorithm. In addition, we introduce an efficient and highly accurate minutiae-based encoding and matching algorithm. We validate the effectiveness of our approach through extensive experimental testing. Our method achieves a minimum Equal Error Rate (EER) of 2.84% on the PolyU contactless fingerprint dataset, demonstrating its superior performance compared to existing state-of-the-art techniques. The proposed fingerprint identification method exhibits notable precision and resilience, proving to be an effective and feasible solution for contactless fingerprint-based identification systems. © 2024 IEEE.

关键词： Frequency estimation

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Enhancing Collaborative and Geometric Multi-Kernel Learning Using Deep Neural Network

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Computers, Materials & Continua 2022年第9期72卷 5099-5116页

作者： Bareera Zafar Syed Abbas Zilqurnain Naqvi Muhammad Ahsan Allah Ditta Ummul Baneen Muhammad Adnan Khan Department of Mechatronics and Control Engineering University of Engineering and TechnologyLahore54890Pakistan Department of Information Sciences Division of Science and TechnologyUniversity of EducationLahore54000Pakistan Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Department Software Gachon UniversitySeongnam13120Korea

This research proposes a method called enhanced collaborative andgeometric multi-kernel learning (E-CGMKL) that can enhance the CGMKLalgorithm which deals with multi-class classification problems with non-lineardata distributions. CGMKL combines multiple kernel learning with softmaxfunction using the framework of multi empirical kernel learning (MEKL) inwhich empirical kernel mapping (EKM) provides explicit feature constructionin the high dimensional kernel space. CGMKL ensures the consistent outputof samples across kernel spaces and minimizes the within-class distance tohighlight geometric features of multiple classes. However, the kernels constructed by CGMKL do not have any explicit relationship among them andtry to construct high dimensional feature representations independently fromeach other. This could be disadvantageous for learning on datasets with complex hidden structures. To overcome this limitation, E-CGMKL constructskernel spaces from hidden layers of trained deep neural networks (DNN).Due to the nature of the DNN architecture, these kernel spaces not onlyprovide multiple feature representations but also inherit the compositionalhierarchy of the hidden layers, which might be beneficial for enhancing thepredictive performance of the CGMKL algorithm on complex data withnatural hierarchical structures, for example, image data. Furthermore, ourproposed scheme handles image data by constructing kernel spaces from aconvolutional neural network (CNN). Considering the effectiveness of CNNarchitecture on image data, these kernel spaces provide a major advantageover the CGMKL algorithm which does not exploit the CNN architecture forconstructing kernel spaces from image data. Additionally, outputs of hiddenlayers directly provide features for kernel spaces and unlike CGMKL, do notrequire an approximate MEKL framework. E-CGMKL combines the consistency and geometry preserving aspects of CGMKL with the compositionalhierarchy of kernel spaces extracted from DNN hidde

关键词： CGMKL multi-class classification deep neural network multiplekernel learning hierarchical kernel spaces

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Medium-assisted enhancement of χc1(3872) production from small to large colliding systems

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Physical Review C 2024年第2期110卷 L021901-L021901页

作者： Yu Guo Xingyu Guo Jinfeng Liao Enke Wang Hongxi Xing Key Laboratory of Atomic and Subatomic Structure and Quantum Control (MOE) Guangdong Basic Research Center of Excellence for Structure and Fundamental Interactions of Matter Institute of Quantum Matter Guangdong-Hong Kong Joint Laboratory of Quantum Matter Guangdong Provincial Key Laboratory of Nuclear Science Southern Nuclear Science Computing Center Physics Department and Center for Exploration of Energy and Matter Southern Center for Nuclear-Science Theory (SCNT)

Studies of exotic hadrons such as the χc1(3872) state provide crucial insights into the fundamental force governing the strong interaction dynamics, with an emerging frontier to investigate their production in high energy collisions where a partonic medium is present. The latest experimental measurements from the Large Hadron Collider show an intriguing evolution pattern of the χc1(3872)-to-ψ(2S) yield ratio from proton-proton collisions with increasing multiplicities toward proton-lead and lead-lead collisions. Here we propose a mechanism of medium-assisted enhancement for the χc1(3872) production, which competes with the more conventional absorption-induced suppression and results in a nonmonotonic trend from small to large colliding systems. Realistic simulations from this model offer a quantitative description of all available data. Predictions are made for the centrality dependence of this observable in PbPb collisions as well as for its system-size dependence from OO and ArAr to XeXe and PbPb collisions. In both cases, a nonmonotonic behavior emerges as the imprint of the competition between enhancement and suppression and can be readily tested by future data.

关键词： Jets & heavy flavor physics Particle & resonance production Charm quark Exotic mesons

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Online Optimization in Power Systems With High Penetration of Renewable Generation:Advances and Prospects

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IEEE/CAA Journal of Automatica Sinica 2023年第4期10卷 839-858页

作者： Zhaojian Wang Wei Wei John Zhen Fu Pang Feng Liu Bo Yang Xinping Guan Shengwei Mei Key Laboratory of System Control and Information Processing Ministry of Education of ChinaDepartment of AutomationShanghai Jiao Tong UniversityShanghai 200240China State Key Laboratory of Power System and the Department of Electrical Engineering Tsinghua UniversityBeijing 100084China Institute of High Performance Computing(IHPC) Agency for Science Technology and Research(A*STAR)Singapore 138632Singapore

Traditionally,offline optimization of power systems is acceptable due to the largely predictable loads and reliable *** increasing penetration of fluctuating renewable generation and internet-of-things devices allowing for fine-grained controllability of loads have led to the diminishing applicability of offline optimization in the power systems domain,and have redirected attention to online optimization ***,online optimization is a broad topic that can be applied in and motivated by different settings,operated on different time scales,and built on different theoretical *** paper reviews the various types of online optimization techniques used in the power systems domain and aims to make clear the distinction between the most common techniques *** particular,we introduce and compare four distinct techniques used covering the breadth of online optimization techniques used in the power systems domain,i.e.,optimization-guided dynamic control,feedback optimization for single-period problems,Lyapunov-based optimization,and online convex optimization techniques for multi-period ***,we recommend some potential future directions for online optimization in the power systems domain.

关键词： optimization Lyapunov optimization online convex optimization online optimization optimization-guided control

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Reproducible Fabrication of Liquid Metal Droplet with FIB Sputtering for Contact Angle Measurement in SEM

Reproducible Fabrication of Liquid Metal Droplet with FIB Sp...

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International Conference on Manipulation, Automation and Robotics at Small Scales (MARSS)

作者： Yuning Lei Sergej Fatikow Department of Computing Science Division Microrobotics and Control Engineering University of Oldenburg Oldenburg Germany

ISBN: (数字)9798350376807

ISBN: (纸本)9798350376814

The wetting phenomenons of surfaces, such as surface tension, are a critical concern for various micro- and nanoelectromechanical systems (MEMS/NEMS) due to scaling effects. In recent years, a lot of efforts have been made in research and industry to study wetting phenomena at the micron and nanometer scales, among which, the liquid metal droplet-based contact angle measurement (CAM) method allows for the characterization of surface wetting properties in a scanning electron microscope (SEM), thus obtaining a resolution that goes beyond the limits of an optical microscope. However, reproducible fabrication of specific micrometer-scale droplets is a key challenge towards the automation of this technique. In this paper, we provide a reliable and reproducible method for the fabrication of specific-sized liquid metal droplets. This includes the generation of droplets at specific locations based on electromigration techniques and the use of focused ion beam (FIB) induced sputtering techniques to fabricate droplets of specific sizes. Using Galinstan as an example, this paper reports its sputtering yield of 0.866 μm 3 .nC −1 , along with the process of determining it. We also demonstrated the reproducibility of fabricating liquid metal droplets with a radius of 10 μm and 5 μm, the results show standard deviations of 0.168 μm and 0.63 μm, respectively.

关键词： Fabrication Electromigration Scanning electron microscopy Liquids Automation Metals Process control

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Artificial Enhanced Image Inpainting for Broadband SMM

Artificial Enhanced Image Inpainting for Broadband SMM

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International Conference on Manipulation, Automation and Robotics at Small Scales (MARSS)

作者： Markus F. Wieghaus Warda Saeed Muhammad Yasir Department of Computing Science Division Microrobotics and Control Engineering University of Oldenburg Oldenburg Germany

ISBN: (数字)9798350376807

ISBN: (纸本)9798350376814

Scanning probe microscopy techniques changed the understanding of micro and nanometer scaled objects. Since the invention of the scanning tunneling microscope and the atomic force microscope, these techniques were further developed and combined with different measurement methodologies such as scanning microwave microscopy. By introducing an electromagnetic signal in the microwave frequency range via the scanning probe microscope tip to the sample, electromagnetic characteristics of the specimen can be acquired. Using a series of frequencies in one scan, even more information like dopant concentration or different layers of material can be captured. The drawback of this method is the long data acquisition time, which cannot be neglected on nanometer scale. To address this issue, we are using reduced raster scan techniques with broadband scanning microwave microscopy and calculate the missing data information by using image inpainting algorithms afterwards. In this work we will present the development of a deep learning algorithm and compare the output of it with different inpainting algorithms, which can even be used in other scanning probe microscope setups.

关键词： Atomic measurements Atomic force microscopy Scanning probe microscopy Neural networks Training data Tunneling Broadband communication

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Improving graph collaborative filtering with network motifs

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Neural computing and Applications 2025年 1-20页

作者： Zhang, Yuqi Yu, Jian Liu, Zhizhong Wang, Guiling Nguyen, Minh Sheng, Quan Z. Wang, Nancy Department of Computer and Information Sciences Auckland University of Technology Auckland Central1010 New Zealand School of Computer and Control Engineering Yantai University Shandong Yantai264005 China School of Information Science and Technology North China University of Technology Beijing100144 China School of Computing Macquarie University SydneyNSW2109 Australia

Deep learning on graphs, specifically graph convolutional networks (GCNs), has exhibited exceptional efficacy in the domain of recommender systems. Most GCNs have a message-passing architecture that enables nodes to aggregate information from neighbours iteratively through multiple layers. This enables GCNs to learn from higher-order information, but the model does not allow for direct captions of the local structural patterns. Our rationale is to investigate the effectiveness of capturing such local patterns for graph-based collaborative filtering to enhance model’s learning ability per layer. This technique combines lower-order and higher-order interactions during layer-wise propagation. In this paper, we propose MotifGCN to aggregate both lower-order and higher-order information in each graph convolution layer. Specifically, we develop dedicated algorithms of generating motif adjacency matrices. The matrices are then used for motif-enhanced neighbourhood aggregation in each layer. As this paper focuses on recommender systems, MotifGCN is built on the basis of bipartite graphs. Our experiments on four real-world datasets show that MotifGCN has a superior performance compared to various state-of-the-art methods. © The Author(s) 2025.

关键词： Collaborative filtering

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Management of Scientific Agricultural Digital Ecosystems Based on Ecosystem Classification 16

Management of Scientific Agricultural Digital Ecosystems Bas...

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16th International Conference Management of Large-Scale System Development, MLSD 2023

作者： Kulba, Vladimir Medennikov, Viktor V.A. Trapeznikov Institute of Control Sciences of the Russian Academy of Sciences Laboratory of Modular Data Processing and Control Systems Moscow Russia Federal Research Center 'Computer Science and Control' of the Russian Academy of Sciences Information and Computing Systems Department Moscow Russia

ISBN: (纸本)9798350337907

The paper considers a mathematical model for managing a scientific digital ecosystem exemplified by the agriculture case;the model enables assessing the impact science exerts on the regions' socioeconomic position in order to build an adequate response to the advanced economies' regime of severe sanctions against Russia. For these purposes, a systemic analysis of the subject identification and classification of ecosystems was conducted to address the critical issue of determining the basic concepts and terms of the country's digitalization. © 2023 IEEE.

关键词： Agriculture

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Who Should I Trust? A Visual Analytics Approach for Comparing Net Load Forecasting Models

Who Should I Trust? A Visual Analytics Approach for Comparin...

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2025 IEEE PES Grid Edge Technologies Conference and Exposition, Grid Edge 2025

作者： Bhattacharjee, Kaustav Kundu, Soumya Chakraborty, Indrasis Dasgupta, Aritra New Jersey Institute of Technology Department of Data Science United States Pacific Northwest National Laboratory Optimization and Control Group United States Lawrence Livermore National Laboratory Center for Applied Scientific Computing United States

ISBN: (纸本)9798350352528

Net load forecasting is crucial for energy planning and facilitating informed decision-making regarding trade and load distributions. However, evaluating forecasting models' performance against benchmark models remains challenging, thereby impeding experts' trust in the model's performance. In this context, there is a demand for technological interventions that allow scientists to compare models across various timeframes and solar penetration levels. This paper introduces a visual analytics-based application designed to compare the performance of deep-learning-based net load forecasting models with other models for probabilistic net load forecasting. This application employs carefully selected visual analytic interventions, enabling users to discern differences in model performance across different solar penetration levels, dataset resolutions, and hours of the day over multiple months. We also present observations made using our application through a case study, demonstrating the effectiveness of visualizations in aiding scientists in making informed decisions and enhancing trust in net load forecasting models. © 2025 IEEE.

关键词： AI/ML net load forecasting visual analytics

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System Analysis of Digital Ecosystem of Russian Agriculture 15

System Analysis of Digital Ecosystem of Russian Agriculture

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15th International Conference Management of Large-Scale System Development, MLSD 2022

作者： Medennikov, Viktor Flerov, Yuri Federal Research Center 'Computer Science and Control' of the Russian Academy of Sciences Information and Computing Systems Department Moscow Russia

ISBN: (数字)9781665497015

ISBN: (纸本)9781665497015

The paper discusses the methods that may be used to set up a science-based digital ecosystem for agriculture;these methods are based on the ideas proposed by A. Kitov and Academician V. Glushkov about a national automated information system (NAIS) for information collection and processing for accounting, planning, and management in the national economy, and substantiated by mathematical modeling. We focus on the need to ontologically integrate two basic digital ecosystem platforms: one for production and another for research and education. © 2022 IEEE.

关键词： Agriculture

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