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35th international Symposium on Algorithms and Computation, ISAAC 2024

作者： Bose, Prosenjit De Carufel, Jean-Lou Stuart, John School of Computer Science Carleton University Ottawa Canada School of Electrical Engineering and Computer Science University of Ottawa Canada

ISBN: (纸本)9783959773546

The standard Delaunay triangulation is a geometric graph whose vertices are points in the plane, and two vertices share an edge if they lie on the boundary of an empty disk. If the disk is replaced with a homothet of a fixed convex shape C, then the resulting graph is called a C-Delaunay graph. We study the problem of local routing in C-Delaunay graphs where C is a regular polygon having five to eight sides. In particular, we generalize the routing algorithm of Chew for square-Delaunay graphs (Chew. SCG 1986, 169-177) in order to obtain the following approximate upper bounds of 4.640, 6.429, 8.531 and 4.054 on the spanning and routing ratios for pentagon-, hexagon-, septagon-, and octagon-Delaunay graphs, respectively. The exact expression for the upper bounds of the routing ratio is Ψ(η) := {√1 + ((cos(2π/n) + n − 1)/ sin(2π/η))2 if n ∈ {5, 6, 7}, 1+ ((cos(π/8) cos(3π/8) + 3)/(cos(π/8) sin(3π/8)))2 if n = 8.. We show that these bounds are tight for the output of our routing algorithm by providing a point set where these bounds are achieved. We also include lower bounds of 1.708 and 1.995 on the spanning and routing ratios of the pentagon-Delaunay graph. Our upper bounds yield a significant improvement over the previous routing ratio upper bounds for this problem, which previously sat at around 400 for the pentagon, septagon, and octagon as well as 18 for the hexagon. Our routing ratios also provide significant improvements over the previously best known spanning ratios for pentagon-, septagon- and octagon-Delaunay graphs, which were around 45. © Prosenjit Bose, Jean-Lou De Carufel, and John Stuart.

关键词： Geometry

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PC-DeepNet: A GNSS Positioning Error Minimization Framework Using Permutation-Invariant Deep Neural Network

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IEEE Sensors Journal 2025年第11期25卷 20764-20777页

作者： Humayun Kabir, M. Hasan, Md. Ali Islam, Md. Shafiqul Ko, Kyeongjun Shin, Wonjae Islamic University Department of Electrical and Electronic Engineering Kushtia7003 Bangladesh Korea University School of Electrical Engineering Seoul02841 Korea Republic of Bangladesh University of Business and Technology Department of Computer Science and Engineering Dhaka1216 Bangladesh Hongik University School of Electronics and Electrical Engineering Seoul04066 Korea Republic of

Global navigation satellite systems (GNSS) face significant challenges in urban and sub-urban areas due to non-line-of-sight (NLOS) propagation, multipath effects, and low received power levels, resulting in highly non-linear and non-Gaussian measurement error distributions. In light of this, conventional model-based positioning approaches, which rely on Gaussian error approximations, struggle to achieve precise localization under these conditions. To overcome these challenges, we put forth a novel learning-based framework, PC-DeepNet, that employs a permutation-invariant (PI) deep neural network (DNN) to estimate position corrections (PC). This approach is designed to ensure robustness against changes in the number and/or order of visible satellite measurements, a common issue in GNSS systems, while leveraging NLOS and multipath indicators as features to enhance positioning accuracy in challenging urban and sub-urban environments. To validate the performance of the proposed framework, we compare the positioning error with state-of-the-art model-based and learning-based positioning methods using two publicly available datasets. The results confirm that proposed PC-DeepNet achieves superior accuracy than existing model-based and learning-based methods while exhibiting lower computational complexity compared to previous learning-based approaches. © 2025 IEEE.

关键词： Global positioning system

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Advancing IoMT Defenses: Deep Collaborative Learning for Robust Healthcare Security

Advancing IoMT Defenses: Deep Collaborative Learning for Rob...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Otoum, Yazan Singh, Paritosh Nayak, Amiya Algoma University School of Computer Science and Technology Canada University of Ottawa School of Electrical Engineering and Computer Science Canada

ISBN: (纸本)9798350351255

The internet of Medical Things (IoMT) plays a pivotal role in healthcare, connecting a myriad of medical devices and applications for efficient patient care. However, the rising prevalence of cyberattacks targeting healthcare institutions for patient data underscores the critical need for robust security measures. This paper introduces a novel Homogenous Collaborative Machine Learning (HCML)-based model to enhance the security of healthcare-connected devices. Utilizing a Deep Neural Network (DNN) algorithm, our model constructs a tailored security framework by integrating multiple device 'edges', enhancing the system's ability to thwart cyber threats. We investigate the impact of incorporating additional edges on the model's performance, employing various metrics and assessing execution times with the IoT healthcare security dataset. Our findings reveal that, compared to conventional centralized learning methods, our proposed HCML model achieves superior generalization, incremental learning, and performance enhancement while maintaining stringent data privacy. This research contributes significantly to the IoMT field by providing a scalable and robust security solution adaptable to the evolving landscape of cyber threats. © 2024 IEEE.

关键词： Cyber attacks

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AtomGAN:unsupervised deep learning for fast and accurate defect detection of 2D materials at the atomic scale

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science China(Information sciences) 2023年第6期66卷 102-114页

作者： Danpeng CHENG Wuxin SHA Zuo XU Shide LI Zhigao YIN Yuling LANG Shun TANG Yuan-Cheng CAO State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronic EngineeringHuazhong University of Science and Technology School of Computer Science and Technology Huazhong University of Science and Technology CITIC Dicastal Co. Ltd.

The extraction of atomic-level material features from electron microscope images is crucial for studying structure-property relationships and discovering new materials. However, traditional electron microscope analyses rely on time-consuming and complex human operations; thus, they are only applicable to images with a small number of atoms. In addition, the analysis results vary due to observers' individual deviations. Although efforts to introduce automated methods have been performed previously, many of these methods lack sufficient labeled data or require various conditions in the detection process that can only be applied to the target material. Thus, in this study, we developed AtomGAN, which is a robust, unsupervised learning method, that segments defects in classical 2D material systems and the heterostructures of MoS2/WS2automatically. To solve the data scarcity problem, the proposed model is trained on unpaired simulated data that contain point and line defects for MoS2/WS2. The proposed AtomGAN was evaluated on both simulated and real electron microscope images. The results demonstrate that the segmented point defects and line defects are presented perfectly in the resulting figures, with a measurement precision of 96.9%. In addition, the cycled structure of AtomGAN can quickly generate a large number of simulated electron microscope images.

关键词： deep learning generative adversarial network defect detection atomic resolution 2D materials

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Enhancing wireless sensing via a target-mounted intelligent reflecting surface

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National science Review 2023年第8期10卷 13-14页

作者： Xiaodan Shao Rui Zhang School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Department of Electrical and Computer Engineering National University of Singapore

An intelligent reflecting surface(IRS),or its various equivalents such as an reconfigurable intelligent surface(RIS), is an emerging technology to control radio signal propagation in wireless systems. An IRS is a digitally controlled metasurface consisting of a large number of passive reflecting elements, which are connected to a smart controller to enable dynamic adjustments of the amplitude and/or phase of the incident signal on each element independently [1].

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T-SNVAE: Deep probabilistic learning with local and global structure for industrial process monitoring

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第6期6卷 1603-1613页

作者： Huang, Jian Liu, Zizhuo Yang, Xu Liu, Yupeng Lv, Zhaomin Peng, Kaixiang Ersoy, Okan K. University of Science and Technology Beijing Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering China University of Science and Technology Beijing Shunde Innovation School Foshan China Shanghai University of Engineering Science School of Urban Railway Transportation China Purdue University Electrical and Computer Engineering West Lafayette United States

Variational autoencoder is a generative deep learning model with a probabilistic structure, which makes it tolerant to process uncertainties and more suitable for process monitoring. However, the probabilistic model may disrupt the topological structure of data and lead to the loss of neighborhood information. To address this issue, a process monitoring approach based on t-distributed stochastic neighbor variational autoencoder (t- SNVAE) is proposed to capture probabilistic features that elucidate both local and global structures within the raw data. Specifically, the distances between neighboring data points are transformed into joint probabilities by using t-SN Embedding. Through minimizing the Kullback-Leibler divergence of joint probabilities between the original data and the reconstructed data, variational autoencoder learns Gaussian features containing both local and global neighborhood information. Finally, monitoring statistics are constructed for monitoring. The efficiency of the proposed approach is verified on a multiphase flow facility and a waste-water treatment process. © 2020 IEEE.

关键词： Embeddings

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FeBT: A Feature Balancing Transformer for Corporate ESG Forecasting

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IEEE Transactions on Knowledge and Data engineering 2025年第7期37卷 4063-4074页

作者： Li, Yawen Zhuang, Mengyu Ye, Guanhua Li, Yan Wang, Junheng Zhou, Jinyi Zhang, Pengfei Beijing University of Posts and Telecommunications School of Economics and Management Beijing China Beijing University of Posts and Telecommunications School of Computer Science National Pilot School of Software Engineering Beijing China University of Electronic Science and Technology of China School of Management and Economics Chengdu China University of Science and Technology Beijing School of Economics and Management Beijing China The University of Queensland School of Electrical Engineering and Computer Science Brisbane Australia

Environmental, social, and governance (ESG) serves as a crucial indicator for evaluating firms in terms of sustainable development. However, the existing ESG evaluation systems suffer from limitations, such as narrow coverage, subjective bias, and lack of timeliness. Therefore, there is a pressing need to leverage machine learning methods to predict the ESG performance of firms using their publicly available data. Traditional machine learning models encounter the feature imbalance problem due to the heterogeneity in ESG-related features. Common approaches typically involve unfolding all features, thereby granting highdimensional folding features greater exposure and accessibility to downstream models, which results in the neglect of lowdimensional features. To fill the research gap regarding fully using the heterogeneous features of enterprises to enhance AIbased ESG prediction performance, we propose the Feature Balancing Transformer (FeBT), a model based on autoencoders and Transformer blocks. FeBT incorporates a novel feature balancing technique that compresses and enhances high-dimensional features from imbalanced data into low-dimensional representations, thereby ensuring a more balanced impact of high-dimensional and low-dimensional features on the model's performance in the downstream ESG forecasting module. Extensive experiments verified the superior performance of FeBT compared with stateof-the-art methods in real-world ESG-related datasets and evidenced that our feature balancing module provides significant insights from high-dimensional folding features. © 1989-2012 IEEE.

关键词： Predictive Models Transformers Data Models Forecasting Accuracy Biological System Modeling Training Time Series Analysis Support Vector Machines Vectors ESG Forecasting Data Masking Time Series Forecasting Machine Learning Models Autoencoder High Dimensional Feature Transformer Block Time Series Support Vector Machine Time Series Data Linear Discriminant Analysis Air Quality Long Short Term Memory Recurrent Neural Network Attention Mechanism Final Prediction Transformer Model Word Embedding XG Boost Gated Recurrent Unit Linear Probe Air Quality Index Positional Encoding Multi Head Attention Mechanism Light GBM Traditional Transformation Compressed Representation Time Series Prediction Attention Heads Prediction Model K Nearest Neighbor Imbalance In Levels Latent Representation

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Optimized Temporary Frequency Support for Wind Power Plants Considering Expanded Operational Region of Wind Turbines

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CSEE Journal of Power and Energy Systems 2025年第1期11卷 51-64页

作者： Zhengyang Hu Bingtuan Gao Zhao Xu Sufan Jiang School of Electrical Engineering Southeast UniversityNanjing210096China Department of Electrical and Electronic Engineering The Hong Kong Polytechnic UniversityHong Kong SARChina Department of Electrical Engineering and Computer Science The University of TennesseeKnoxvilleTN 37996 USA

Wind power plants(WPPs)are increasingly mandated to provide temporary frequency support to power systems during contingencies involving significant power ***,the frequency support capabilities of WPPs under derated operations remain insufficiently investigated,highlighting the potential for further improvement of the frequency *** paper proposes a bi-level optimized temporary frequency support(OTFS)strategy for a *** implementation of the OTFS strategy is collaboratively accomplished by individual wind turbine(WT)controllers and the central WPP ***,to exploit the frequency support capability of WTs,the stable operational region of WTs is expanded by developing a novel dynamic power control approach in WT *** approach synergizes the WTs'temporary frequency support with the secondary frequency control of synchronous generators,enabling WTs to release more kinetic energy without causing a secondary frequency ***,a model predictive control strategy is developed for the WPP *** strategy ensures that multiple WTs operating within the expanded stable region are coordinated to minimize the magnitude of the frequency drop through efficient kinetic energy ***,comprehensive case studies are conducted on a real-time simulation platform to validate the effectiveness of the proposed strategy.

关键词： Active power control model predictive control optimized temporary frequency support wind power plant wind turbine

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Regression analysis of squeezing-induced hybrid nanofluid flow in Darcy-Forchheimer porous medium

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Applied Mathematics and Mechanics(English Edition) 2025年第1期46卷 193-208页

作者： K.MUHAMMAD M.SARFRAZ Department of Humanities and Sciences School of Electrical Engineering and Computer ScienceNational University of Sciences and TechnologyIslamabad 44000Pakistan Department of Mathematics Quaid-i-Azam UniversityIslamabad 44000Pakistan

This article presents a mathematical model addressing a scenario involving a hybrid nanofluid flow between two infinite parallel *** plate remains stationary,while the other moves downward at a squeezing *** space between these plates contains a Darcy-Forchheimer porous medium.A mixture of water-based fluid with gold(Au)and silicon dioxide(Si O2)nanoparticles is *** contrast to the conventional Fourier's heat flux equation,this study employs the Cattaneo-Christov heat flux equation.A uniform magnetic field is applied perpendicular to the flow direction,invoking magnetohydrodynamic(MHD)***,the model accounts for Joule heating,which is the heat generated when an electric current passes through the *** problem is solved via NDSolve in *** and statistical analyses are conducted to provide insights into the behavior of the nanomaterials between the parallel plates with respect to the flow,energy transport,and skin *** findings of this study have potential applications in enhancing cooling systems and optimizing thermal management *** is observed that the squeezing motion generates additional pressure gradients within the fluid,which enhances the flow rate but reduces the frictional ***,the fluid is pushed more vigorously between the plates,increasing the flow *** the fluid experiences higher flow rates due to the increased squeezing effect,it spends less time in the region between the *** thermal relaxation,however,abruptly changes the temperature,leading to a decrease in the temperature fluctuations.

关键词： convective boundary condition Darcy-Forchheimer medium hybrid nanofuid Joule heating magnetohydrodynamic(MHD) numerical solution squeezing flow regression analysis

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Designing an IT2 Fuzzy Rule-based System for Emotion Recognition Using Biological Data 14

Designing an IT2 Fuzzy Rule-based System for Emotion Recogni...

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14th international Conference on computer and Knowledge engineering, ICCKE 2024

作者： Keshtkar, Mahsa Tahayori, Hooman Department of Computer Science and Engineering IT School of Electrical and Computer Engineering Shiraz University Shiraz Iran

ISBN: (纸本)9798331511272

— In recent years, significant efforts have been dedicated to detect human emotions. This interest primarily stems from the fact that emotions influence individuals' reactions and behaviors. Understanding these impacts can be beneficial in various fields, ranging from developing healthcare surveillance systems for the elderly to identifying adverse emotions in those engaged in high-stakes occupations, such as pilots, sailors, and long-haul truckers. This paper presents a solution based on the application of interval type two fuzzy sets and systems to detect individuals' emotions using their physiological signals. The physiological signals on the same emotion varies from person to person and even for the same person in different situations. This variability has led us to employ interval type-2 fuzzy sets. We defined five footprints of uncertainty for each physiological signal to cover its whole range and employed instance selection technique to optimize the rule base. We develop a rule-based fuzzy system that employs the fewest feasible number of rules for the purpose of emotion recognition. In order to enhance the comprehensibility of the system, we have also utilized a minimal set of features in the construction of the rules. This study implemented on physiological signals from the K-EmoCon dataset. Obtained results shows that the proposed method outperform other existing method with lower computational complexity. ©2024 IEEE.

关键词： Fuzzy rules

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