检索结果-内蒙古大学图书馆

3rd International Conference on Sustainable Civil engineering and Architecture, ICSCEA 2023

作者： Luong, Van Hai Nguyen, H.P. Nguyen-Thoi, T. 268 Ly Thuong Kiet Street District 10 Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Linh Trung Ward Thu Duc District Ho Chi Minh City Viet Nam School of Civil Engineering The University of Queensland St LuciaQLD4072 Australia Laboratory for Applied and Industrial Mathematics Institute for Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh City Viet Nam Faculty of Mechanical-Electrical and Computer Engineering Van Lang University Ho Chi Minh City Viet Nam

ISBN: (纸本)9789819974337

The cell-based smoothed discrete shear gap method (CS-DSG3) was introduced for analysing Mindlin plates. This method employs three-node triangular elements and can produce solutions with high accuracy. This research aims to combine CS-DSG3 with the Boundary Element Method (BEM) to perform hydroelastic analyses on pontoon-type Very Large Floating Structures (VLFS) with varying plan shapes. In this study, the VLFS is conceptualized as a Mindlin plate resting on an ideal fluid. The structural and fluid domains are discretized using three-node triangular elements. The structural equation is formulated using CS-DSG3, while the water equation is established using BEM. The hydroelastic analysis is conducted within the frequency domain. To validate the accuracy of the CS-DSG3/BEM approach, its results are compared with available published solutions. The proposed method is then employed in parametric investigations involving various VLFS plan shapes to explore how the shape influences hydroelastic responses. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Boundary element method

来源：评论

学校读者我要写书评

暂无评论

Continuous Image Outpainting with Neural Ode

SSRN

引用

SSRN 2023年

作者： Gao, Penglei Yang, Xi Zhang, Rui Huang, Kaizhu Department of Computer Science University of Liverpool United Kingdom School of Advanced Technology Canada Department of Foundational Mathematics Xi’an Jiaotong-Liverpool University China Institute of Applied Physical Sciences and Engineering Duke Kunshan University China

Generalised image outpainting is an important and active research topic in computer vision and pattern recognition, which aims to extend appealing content all-side around a given image. Existing state-of-the-art outpainting methods often rely on discrete extrapolation to extend the feature map in the bottleneck. They thus suffer from content unsmoothness, especially in circumstances where the outlines of objects in the extrapolated regions are incoherent with the input sub-images. To mitigate this issue, we design a novel bottleneck with Neural ODEs to make continuous extrapolation in latent space, which could be a plug-in for many deep learning frameworks. Our ODE-based network continuously transforms the state and makes accurate predictions by learning the incremental relationship among latent points, leading to both smooth and structured feature representation. Experimental results on three real-world datasets both applied on transformer-based and CNN-based frameworks show that our methods could generate more realistic and coherent images against the state-of-the-art image outpainting approaches. © 2023, The Authors. All rights reserved.

关键词： Extrapolation

来源：评论

学校读者我要写书评

暂无评论

Persistent de Rham-Hodge Laplacians in Eulerian representation for manifold topological learning

arXiv

引用

arXiv 2024年

作者： Su, Zhe Tong, Yiying Wei, Guo-Wei Department of Mathematics Michigan State University East LansingMI48824 United States Department of Computer Science and Engineering Michigan State University East LansingMI48824 United States Department of Biochemistry and Molecular Biology Michigan State University East LansingMI48824 United States Department of Electrical and Computer Engineering Michigan State University East LansingMI48824 United States

Recently, topological data analysis has become a trending topic in data science and engineering. However, the key technique of topological data analysis, i.e., persistent homology, is defined on point cloud data, which does not work directly for data on manifolds. Although earlier evolutionary de Rham-Hodge theory deals with data on manifolds, it is inconvenient for machine learning applications because of the numerical inconsistency caused by remeshing the involving manifolds in the Lagrangian representation. In this work, we introduce persistent de Rham-Hodge Laplacian, or persistent Hodge Laplacian (PHL) as an abbreviation, for manifold topological learning. Our PHLs are constructed in the Eulerian representation via structure-persevering Cartesian grids, avoiding the numerical inconsistency over the multiscale manifolds. To facilitate the manifold topological learning, we propose a persistent Hodge Laplacian learning algorithm for data on manifolds or volumetric data. As a proof-of-principle application of the proposed manifold topological learning model, we consider the prediction of protein-ligand binding affinities with two benchmark datasets. Our numerical experiments highlight the power and promise of the proposed method. Copyright © 2024, The Authors. All rights reserved.

关键词： Topology

来源：评论

学校读者我要写书评

暂无评论

Smart Medical Pills Dispenser

Smart Medical Pills Dispenser

引用

International Conference on Developments in eSystems engineering (DESE)

作者： Khawla A. Alnajjar Abdulaziz Altamimi Khalifa Altamimi Ahmed Alshehhi Sam Ansari Soliman Mahmoud Abir Jaafar Hussain Department of Electrical Engineering University of Sharjah Sharjah United Arab Emirates University of Khorfakkan Khorfakkan United Arab Emirates School of Computer Science and Mathematics Faculty of Engineering Liverpool John Moores University Liverpool United Kingdom

Medication non-adherence is a prevalent concern, particularly among individuals managing chronic illnesses who rely on consistent pill consumption. This study addresses forgetfulness and non-compliance in medication intake by proposing a system that ensures accurate administration of prescribed medications at designated times. This paper investigates medication adherence challenges, primarily focusing on chronic condition management. Leveraging mobile phones, our innovative approach aims to mitigate these challenges. This paper proposes a system that delivers timely reminders to patients via mobile devices, fostering responsibility toward adhering to medication regimens. Central to the proposed solution is a patient-to-hospital communication framework, enabling caregivers to curate medication schedules. Caregivers have control over medications, timings, and dosages. This empowers short-term and long-term medication users to monitor regimens, alleviating concerns of omissions or deviations. Implications of the proposed framework are far-reaching. Consistent medication adherence can enhance therapeutic interventions, potentially reducing morbidity and mortality. The presented technology-healthcare convergence underscores the positive impact of technological interventions in medical contexts.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Strain-Balanced AlScN/GaN HEMTs with fT/fMAX of 173/321 GHz

Strain-Balanced AlScN/GaN HEMTs with fT/fMAX of 173/321 GHz

引用

International Electron Devices Meeting (IEDM)

作者： T. S. Nguyen K. Nomoto W. Zhao C. Savant H. G. Xing D. Jena Department of Materials Science and Engineering Cornell University Ithaca NY USA School of Electrical and Computer Engineering Cornell University Ithaca NY USA School of Applied and Engineering Physics Cornell University Ithaca NY USA Kavli Institute at Cornell for Nanoscale Science Cornell University Ithaca NY USA

ISBN: (数字)9798350365429

ISBN: (纸本)9798350365436

We report the effect of scaling the gate length from 0.8 µm to 40 nm on the performance of novel strain-balanced AlScN/GaN high electron mobility transistors (HEMTs) on SiC substrates. A new strain-balanced heterostructure is introduced where the tensile strain of the AlN interlayer is balanced by the compressive strain in the AlScN barrier layer. MBE regrown source/drain ohmics with ultralow contact resistance of 0.09 Ω·mm allow a low HEMT on resistance of 0.83 Ω·mm. The shortest gate length AlScN/GaN HEMTs exhibit maximum drain currents of 2.8 A/mm, peak transconductance of 0.55 S/mm, and speed characterized by $f_{T}$ / $f_{MAX}$ of 173/321 GHz. The highest $f_{MAX}$ obtained here is ~2X higher than previous AlScN HEMTs.

关键词： Resistance Tensile strain Silicon carbide HEMTs Logic gates MODFETs III-V semiconductor materials Electron devices Transconductance Substrates

来源：评论

学校读者我要写书评

暂无评论

Optimizing Spectrum Prediction in Cognitive Radio: Genetic Algorithm-Enhanced Neural Networks and Radial Basis Functions

Optimizing Spectrum Prediction in Cognitive Radio: Genetic A...

引用

International Conference on Developments in eSystems engineering (DESE)

作者： Sam Ansari Antanios Kaissar Tarek Khater Khawla A. Alnajjar Soliman Mahmoud Abir Hussain Department of Electrical Engineering University of Sharjah Sharjah United Arab Emirates University of Khorfakkan Khorfakkan United Arab Emirates School of Computer Science and Mathematics Faculty of Engineering Liverpool John Moores University Liverpool United Kingdom

Throughout recent years, the field of wireless communication has experienced exponential growth. This expansion has been propelled by the continual innovation of diverse wireless standards and the evolution of high-speed applications, resulting in a mounting scarcity of spectrum and an intensified demand for bandwidth. Regrettably, existing studies substantiate an inefficient utilization of available frequency bands. Channel bandwidth and effective spectrum utilization persist as formidable challenges in the realm of wireless communication. Addressing these challenges, cognitive radio stands as a pivotal solution, enabling the efficient sharing of available spectrum among primary/secondary or licensed/unlicensed users. The successful implementation of cognitive radio relies significantly on accurate spectrum sensing and prediction to avert interference or collisions among users. This work introduces a neural network-based model augmented and fine-tuned by a genetic algorithm, exemplifying state-of-the-art effectiveness in spectrum prediction. To expand the horizon, this paper investigates a novel approach based on the radial basis function network, further enriching the exploration. The proposed model demonstrates exceptional performance as validated through rigorous MATLAB simulations. The comparative analysis of these simulations serves as a robust benchmark, illuminating the superior efficacy and practicality of the model in real-world scenarios.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Inequity aversion reduces travel time in the traffic light control problem

arXiv

引用

arXiv 2023年

作者： Hassanjani, Mersad Alamiyan-Harandi, Farinaz Ramazi, Pouria Department of Electrical & Computer Engineering Isfahan University of Technology Isfahan84156-83111 Iran Department of Mathematics & Statistics Brock University St. CatharinesONL2S 3A1 Canada

The traffic light control problem is to improve the traffic flow by coordinating between the traffic lights. Recently, a successful deep reinforcement learning model, CoLight, was developed to capture the influences of neighboring intersections by a graph attention network. We propose IACoLight that boosts up to 11.4% the performance of CoLight by incorporating the Inequity Aversion (IA) model that reshapes each agent's reward by adding or subtracting advantageous or disadvantageous reward inequities compared to other agents. Unlike in the other applications of IA, where both advantageous and disadvantageous inequities are punished by considering negative coefficients, we allowed them to be also rewarded and explored a range of both positive and negative coefficients. Our experiments demonstrated that making CoLight agents averse to inequities improved the vehicles' average travel time and rewarding rather than punishing advantageous inequities enhanced the results. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Employing the Concept of Stacking Ensemble Learning to Generate Deep Dream Images Using Multiple Cnn Variants

SSRN

引用

SSRN 2024年

作者： Alkhazraji, Lafta Abbas, Ayad R. Jamil, Abeer S. Kadhim, Zahraa Saddi Alkhazraji, Wissam Jebur, Sabah Abdulazeez Shaker, Bassam Noori Mohammed, Mohammed Abdallazez Mohammed, Mohanad A. Al-Araji, Basim Mohammed Mohmmed, Abdulkareem Z. Khan, Wasiq Khan, Bilal Hussain, Abir Jaafar Department of Medical Instrumentation Engineering Techniques Imam Ja'afar Al-Sadiq University Iraq Department of Computer Science University of Technology Baghdad Iraq Department of Computer Technology Engineering Al-Mansour University College Baghdad Iraq Department of Computer Techniques Engineering Imam Alkadhim University College Baghdad Iraq Computer Science Department College of Computer Science and Information Technology University of Al-Qadisiyah Al Diwaniyah Iraq University of Karbala College of Computer Science and Information Technology Computer Science Department Iraq Computer Science Department University of Technology Baghdad Iraq Imam Ja'afar Al-Sadiq University Iraq Babylon Education Directorate Ministry of Education Babylon Iraq School of Computer Science and Mathematics Liverpool John Moores University United Kingdom School of Computer and Engineering California State University San Bernardino United States Department of Electrical Engineering University of Sharjah Sharjah United Arab Emirates

Addiction and adverse effects resulting from schizophrenia are rapidly becoming a global issue, necessitating the development of advanced approaches that can provide support to psychiatrists and psychologists to understand and replicate the hallucinations and imagery experienced by patients. Such approaches can also be useful for promoting interest in human artwork, particularly surrealist images. Accordingly, in the present, a stacking ensemble Deep Dream model was developed that aids psychiatrists and psychologists in addressing the challenge of mimicking hallucinations. The dream-like images generated in the present study possess an aesthetic quality reminiscent of surrealist art. For model development, a series of five pre-trained Convolutional Neural Network (CNN) architectures—VGG-19, Inception v3, VGG-16, Inception-ResNet-V2, and Xception were stacked in an ensemble learning approach to create Deep Dream images whereby the upper hidden layers of the architectures were activated, and the models were trained via the Adam optimizer. Performance of the proposed model was evaluated across three octaves to amplify the maximum possible patterns and features of the base image. The resulting dream-like images contain shapes that reflect elements from the ImageNet dataset on which the above pre trained models were trained. Each of the base images was manipulated to generate various dreamed images, each one with three octaves, which were finally combined to construct the final image with its loss. Final Deep Dream image showed a loss of 47.5821, while still retaining some features from the base image. © 2024, The Authors. All rights reserved.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

TEMPORAL EVOLUTION IN SYNTHETIC HANDWRITING

arXiv

引用

arXiv 2024年

作者： Carmona-Duarte, Cristina Ferrer, Miguel A. Parziale, Antonio Marcelli, Angelo Instituto Universitario para el Desarrollo Tecnológico y la Innovación Universidad de Las Palmas de Gran Canaria Spain Natural Computation Lab Department of Information and Electrical Engineering and Applied Mathematics University of Salerno Italy

New methods for generating synthetic handwriting images for biometric applications have recently been developed. The temporal evolution of handwriting from childhood to adulthood is usually left unexplored in these works. This paper proposes a novel methodology for including temporal evolution in a handwriting synthesizer by means of simplifying the text trajectory plan and handwriting dynamics. This is achieved through a tailored version of the kinematic theory of rapid human movements and the neuromotor inspired handwriting synthesizer. The realism of the proposed method has been evaluated by comparing the temporal evolution of real and synthetic samples both quantitatively and subjectively. The quantitative test is based on a visual perception algorithm that compares the letter variability and the number of strokes in the real and synthetic handwriting produced at different ages. In the subjective test, 30 people are asked to evaluate the perceived realism of the evolution of the synthetic handwriting. © 2024, CC BY-NC-ND.

关键词： Kinematics

来源：评论

学校读者我要写书评

暂无评论

Bayesian Data Fusion with Shared Priors

SSRN

引用

SSRN 2022年

作者： Wu, Peng Imbiriba, Tales Elvira, Víctor Closas, Pau Electrical and Computer Engineering Department Northeastern University BostonMA02115 United States School of Mathematics University of Edinburgh United Kingdom

The integration of data and knowledge from several sources is known as data fusion. When data is available in a distributed fashion or when different sensors are used to infer a quantity of interest, data fusion becomes essential. In Bayesian settings, a priori information of the unknown quantities is available and, possibly, shared among the distributed estimators. When the local estimates are fused, such prior might be overused unless it is accounted for. This paper explores the effects of shared priors in Bayesian data fusion contexts, providing fusion rules and analysis to understand the performance of such fusion as a function of the number of collaborative agents and the uncertainty of the priors. Analytical results are corroborated through experiments in a variety of estimation and classification problems. © 2022, The Authors. All rights reserved.

关键词： Data fusion

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：