检索结果-内蒙古大学图书馆

arXiv 2022年

作者： Santana, Ítalo Serrano, Breno Schiffer, Maximilian Vidal, Thibaut Department of Computer Science Pontifical Catholic University of Rio de Janeiro Brazil TUM School of Management Technical University of Munich Germany Munich Data Science Institute Technical University of Munich Germany CIRRELT Department of Mathematics and Industrial Engineering Polytechnique Montréal Canada SCALE-AI Chair in Data-Driven Supply Chains

The classical hinge-loss support vector machines (SVMs) model is sensitive to outlier observations due to the unboundedness of its loss function. To circumvent this issue, recent studies have focused on non-convex loss functions, such as the hard-margin loss, which associates a constant penalty to any misclassified or within-margin sample. Applying this loss function yields much-needed robustness for critical applications but it also leads to an NP-hard model that makes training difficult, since current exact optimization algorithms show limited scalability, whereas heuristics are not able to find high-quality solutions consistently. Against this background, we propose new integer programming strategies that significantly improve our ability to train the hard-margin SVM model to global optimality. We introduce an iterative sampling and decomposition approach, in which smaller subproblems are used to separate combinatorial Benders’ cuts. Those cuts, used within a branch-and-cut algorithm, permit to converge much more quickly towards a global optimum. Through extensive numerical analyses on classical benchmark data sets, our solution algorithm solves, for the first time, 117 new data sets to optimality and achieves a reduction of 50% in the average optimality gap for the hardest datasets of the benchmark. Copyright © 2022, The Authors. All rights reserved.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

AI-DRIVEN DAY-TO-DAY ROUTE CHOICE

arXiv

引用

arXiv 2024年

作者： Wang, Leizhen Duan, Peibo He, Zhengbing Lyu, Cheng Chen, Xin Zheng, Nan Yao, Li Ma, Zhenliang Department of Data Science and Artificial Intelligence Monash University Melbourne Australia Laboratory for Information & Decision Systems Massachusetts Institute of Technology Cambridge United States Chair of Transportation Systems Engineering Technical University of Munich Munich Germany School of Civil Engineering The University of Queensland Brisbane Australia Department of Civil Engineering Monash University Melbourne Australia School of Computer Science and Engineering Southeast University Nanjing China Department of Civil and Architectural Engineering KTH Royal Institute of Technology Stockholm Sweden

Understanding travelers’ route choices can help policymakers devise optimal operational and planning strategies for both normal and abnormal circumstances. However, existing choice modeling methods often rely on predefined assumptions and struggle to capture the dynamic and adaptive nature of travel behavior. Recently, Large Language Models (LLMs) have emerged as a promising alternative, demonstrating remarkable ability to replicate human-like behaviors across various fields. Despite this potential, their capacity to accurately simulate human route choice behavior in transportation contexts remains doubtful. To satisfy this curiosity, this paper investigates the potential of LLMs for route choice modeling by introducing an LLM-empowered agent, "LLMTraveler." This agent integrates an LLM as its core, equipped with a memory system that learns from past experiences and makes decisions by balancing retrieved data and personality traits. The study systematically evaluates the LLMTraveler’s ability to replicate human-like decision-making through two stages of day-to-day (DTD) congestion games: (1) analyzing its route-switching behavior in single origin-destination (OD) pair scenarios, where it demonstrates patterns that align with laboratory data but cannot be fully explained by traditional models, and (2) testing its capacity to model adaptive learning behaviors in multi-OD scenarios on the Ortuzar and Willumsen (OW) network, producing results comparable to Multinomial Logit (MNL) and Reinforcement Learning (RL) models. Additionally, the study assesses lightweight, open-source LLMs, highlighting their effectiveness in route choice simulation and their potential as cost-effective alternatives to more advanced closed-source models. These experiments demonstrate that the framework can partially replicate human-like decision-making in route choice while providing natural language explanations for its decisions. This capability offers valuable insights for transportation poli

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

A Hybrid Neural Network with Smart Skip Connections for High-Precision, Low-Latency EMG-Based Hand Gesture Recognition

A Hybrid Neural Network with Smart Skip Connections for High...

引用

IEEE International Conference on Multi Topic

作者： Hafsa Wazir Jawad Ahmad Muazzam A. Khan Sana Ullah Jan Fadia Ali Khan Muhammad Shahbaz Khan ICESCO Chair for Big Data Analytics and Edge Computing Quaid-i-Azam University Islamabad Pakistan Cybersecurity Center Prince Mohammad Bin Fahd University Alkhobar Saudi Arabia School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh UK Department of Computer Science HITEC University Taxila Pakistan

ISBN: (数字)9798331507213

ISBN: (纸本)9798331507220

Electromyography (EMG) is extensively used in key biomedical areas, such as prosthetics, and assistive and interactive technologies. EMG signals measure the electrical activity of muscles during different motions. EMG signals play a key role in gesture recognition studies, such as hand gesture recognition. This paper presents a new hybrid neural network named ConSGruNet for precise and efficient hand gesture recognition. The proposed model comprises convolutional neural networks with smart skip connections in conjunction with a Gated Recurrent Unit (GRU). The proposed model is trained on the complete Ninapro DB1 dataset. The proposed model boasts an accuracy of 99.7% in classifying 53 classes in just 25 milliseconds. In addition to being fast, the proposed model is lightweight with just 3,946 KB in size. The fast inference time and lightweight architecture of the proposed model makes it suitable for resource constrained IoT devices. Moreover, the proposed model has also been evaluated for the reliability parameters, i.e., Cohen’s kappa coefficient, Matthew’s correlation coefficient, and confidence intervals. The close to ideal results of these parameters validate the models performance on unseen data.

关键词： Hands Performance evaluation Correlation coefficient Accuracy Neural networks Gesture recognition Electromyography data models Internet of Things Reliability

来源：评论

学校读者我要写书评

暂无评论

Optimal Decision Diagrams for Classification

arXiv

引用

arXiv 2022年

作者： Florio, Alexandre M. Martins, Pedro Schiffer, Maximilian Serra, Thiago Vidal, Thibaut CIRRELT SCALE-AI Chair in Data-Driven Supply Chains Department of Mathematical and Industrial Engineering Polytechnique Montréal Canada Department of Computer Science Pontifical Catholic University of Rio de Janeiro Brazil School of Management Munich Data Science Institute Technical University of Munich Germany Freeman College of Management Bucknell University United States

Decision diagrams for classification have some notable advantages over decision trees, as their internal connections can be determined at training time and their width is not bound to grow exponentially with their depth. Accordingly, decision diagrams are usually less prone to data fragmentation in internal nodes. However, the inherent complexity of training these classifiers acted as a long-standing barrier to their widespread adoption. In this context, we study the training of optimal decision diagrams (ODDs) from a mathematical programming perspective. We introduce a novel mixed-integer linear programming model for training and demonstrate its applicability for many datasets of practical importance. Further, we show how this model can be easily extended for fairness, parsimony, and stability notions. We present numerical analyses showing that our model allows training ODDs in short computational times, and that ODDs achieve better accuracy than optimal decision trees, while allowing for improved stability without significant accuracy *** Codes 68T99 Copyright © 2022, The Authors. All rights reserved.

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

Fatigue prediction for metallic additively manufactured lattice components using FCM based on average strain energy density approach

Fatigue prediction for metallic additively manufactured latt...

引用

International Congress on Advanced Electromagnetic Materials in Microwaves and Optics (METAMATERIALS)

作者： R. De Biasi O. Oztoprak F. Zanini S. Carmignato F. Berto S. Kollmannsberger M. Benedetti Department of Industrial Engineering Via Sommarive 9 University of Trento Trento Italy School of Engineering and Design Chair of Computational Modeling and Simulation Arcisstraße 21 Technical University of Munich Munich Germany Department of Management and Engineering Stradella San Nicola 3 University of Padova Vicenza Italy Department of Chemical Engineering Materials Environment Via Eudossiana 18 University of Rome Sapienza Roma Italy Faculty of Civil and Environmental Sciences Chair of Data Science in Engineering Coudraystraße 13b Bauhaus-Universität Weimar Weimar Germany

ISBN: (数字)9798350373493

ISBN: (纸本)9798350373509

An accurate prediction of the fatigue life of metallic additively manufactured lattice structures is still a challenging task. The proposed work addresses this challenge of industrial relevance through the computation of an as-built geometry of a lattice structure directly from its X-ray computed tomography reconstruction. The computation is carried out by taking advantage of the Finite Cell Method and applies the average strain energy density method to predict fatigue failure. The proof of concept provided in this work shows an excellent agreement in the fatigue prediction, paving the way for a computationally accurate fatigue life prediction of other types of metamaterials.

关键词： Geometry Accuracy Lattices Fatigue Metamaterials Topology X-ray imaging Stress Strain Testing

来源：评论

学校读者我要写书评

暂无评论

Capturing thin-film microstructure contributions during ultrafast laser-metal interactions using atomistic simulations

引用

Materials & Design 2025年 256卷

作者： Hariprasath Ganesan Stefan Sandfeld Institute for Advanced Simulations – Materials Data Science and Informatics (IAS-9) Forschungszentrum Juelich Juelich Germany RWTH Aachen University Faculty of Georesources and Materials Engineering Chair of Materials Data Science and Materials Informatics Aachen Germany

Progress in the emerging fields of atomic and close-to-atomic scale manufacturing is underpinned by enhanced precision and optimization of laser-controlled nanostructuring. Understanding thin films' crystallographic orientations and microstructure effects becomes crucial for optimizing the laser-metallic thin film interactions; however, these effects remain largely unexplored at the atomic scale. Using a hybrid two-temperature model and molecular dynamics, we simulated ultrafast laser-metal interactions for gold thin films with varying crystallographic orientations and microstructure configurations. Microstructure features, namely grain size, grain topology, and local crystallographic orientation, controlled the rate and extent of lattice disorder evolution and phase transformation, particularly at lower applied fluences. Our simulations provided comprehensive insights encompassing both the nanomechanical and thermodynamic aspects of ultrafast laser-metal interactions at atomic resolution. Microstructure-aware/informed thin film fabrication and targeted defect engineering could improve the precision of nanoscale laser processing and potentially emerge as an energy-efficient optimization strategy.

关键词： Femtosecond laser Au thin-film Laser-metal interaction Molecular dynamics Microstructure

来源：评论

学校读者我要写书评

暂无评论

CBAM_SAUNet: A novel attention U-Net for effective segmentation of corner cases

CBAM_SAUNet: A novel attention U-Net for effective segmentat...

引用

Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Srividya Tirunellai Rajamani Kumar Rajamani Angeline J Karthika R Björn W. Schuller Chair of Embedded Intelligence for Health Care & Wellbeing University of Augsburg Germany Department of Artificial Intelligence Marwadi University Rajkot Gujarat India Department of Electronics and Communication Engineering Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham India CHI - Chair of Health Informatics MRI Technische Universität München (TUM) Germany Munich Data Science Institute Germany Munich Center for Machine Learning and GLAM - the Group on Language Audio & Music Imperial College London London UK

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

U-Net has been demonstrated to be effective for the task of medical image segmentation. Additionally, integrating attention mechanism into U-Net has been shown to yield significant benefits. The Shape Attentive U-Net (SAUNet) is one such recently proposed attention U-Net that also focuses on interpretability. Furthermore, recent research has focused on identification and reporting of corner cases in segmentation to accelerate the utilisation of deep learning models in clinical practise. However, achieving good model performance on such corner cases is a less-explored research area. In this paper, we propose CBAM_SAUNet which enhances the dual attention decoder block of SAUNet to improve its performance on corner cases. We achieve this by utilising a novel variant of the Convolutional Block Attention Module (CBAM)’s channel attention in the decoder block of SAUNet. We demonstrate the effectiveness of CBAM_SAUNet in the Automated Cardiac Diagnosis Challenge (ACDC) cardiac MRI segmentation challenge. Our proposed novel approach results in improvement in the Dice scores of 12% for Left Ventricle (LV) as well as Right Ventricle (RV) segmentation and 8% for Myocardium (MYO) for the identified corner-case dataset.

关键词： Deep learning Image segmentation Attention mechanisms Shape Magnetic resonance imaging Myocardium Decoding Medical diagnostic imaging

来源：评论

学校读者我要写书评

暂无评论

Powder bed fusion electron beam of an oxygen-compatible β-Ti–35.5Nb–2Ta–3Zr alloy: Feasibility and material evaluation

引用

European Journal of Materials 2025年第1期5卷

作者： Rittinghaus, Silja-Katharina Malladi, Sri Bala Aditya Rackel, Marcus Willi Borchert, Daniel Shokri, Hamed Ratschbacher, Karin Budnitzki, Michael Gökce, Bilal Hryha, Eduard Chair of Materials Science and Additive Manufacturing School of Mechanical Engineering and Safety Engineering University of Wuppertal Wuppertal Germany Department of Industrial and Materials Science Chalmers University of Technology Gothenburg Sweden Metal Physics Helmholtz-Zentrum Hereon Geesthacht Germany GfE Metalle und Materialien GmbH Nürnberg Germany Institute for Advanced Simulations—Materials Data Science and Informatics (IAS-9) Forschungszentrum Jülich GmbH Jülich Germany

Additive manufacturing (AM) by powder bed fusion electron beam (PBF-EB) is a state-of-the-art method for processing titanium-based alloys, especially due to the high inherent oxygen affinity of the material. Ti–Nb–Ta–Zr alloys provide, dependent on composition and manufacturing, a versatile property profile and are of increasing interest as structural materials as AM eases their production. Enhancing sustainability through reduced purity requirements, alongside improved material performance, could lead to more efficient and environmentally-friendly material design. This study explores the feasibility of producing dense β-Ti–35.5Nb–2Ta–3Zr alloys with an elevated oxygen content of 2800 ppm through PBF-EB, achieving up to 99.7% density and notable mechanical properties such as a hardness of 330HV0.3. The microstructure was found to be single-β in all samples with low segregation of constitutional elements. Indications of a fibre texture switch, depending on the chosen process parameters, were found. © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： additive manufacturing microstructure TNTZ β-titanium

来源：评论

学校读者我要写书评

暂无评论

A spectrum-enhanced attention model for semantic segmentation of remote sensing images

A spectrum-enhanced attention model for semantic segmentatio...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Xin Li Feng Xu Feifei Tao Yao Tong Xin Lyu Jianyi Zhong André Kaup College of Computer Science and Software Engineering Hohai University Nanjing China Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China School of Computer Engineering Jiangsu Ocean University Lianyungang China School of Artificial Intelligence and Information Technology Nanjing University of Chinese Medicine Nanjing China Chair of Multimedia Communications and Signal Processing Friedrich-Alexander University Erlangen-Nuremberg Erlangen Germany

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Semantic segmentation of remote sensing images (RSIs) is essential for applications such as environmental monitoring, urban planning, and disaster management. Convolutional Neural Networks (CNNs) and their variants struggle to capture comprehensive spectral context for learning discriminative representations. In this paper, we propose a Spectrum-Enhanced Network (SPENet) that leverages the Frequency Transformer Block (FTB) to capture rich spectral context. FTB integrates Spectrum-Enhanced Attention (SEA) with Multi-Head Frequency Self-Attention (MH-FSA), incorporating more informative contextual cues. Specifically, SEA aggregates spectral statistics through covariance matrix normalization before applying channel-wise attention. By projecting feature maps onto the frequency domain, MH-FSA provides the network with a broader context, extending beyond the low-frequency focus of standard self-attention mechanisms. Extensive experiments on the ISPRS Potsdam and LoveDA datasets show that SPENet significantly outperforms state-of-the-art methods. Besides, the proposed SEA module notably rises average F1-score/overall accuracy/mean insert over union wiht more than 2.5/2.6%/2.3%, as demonstrated by ablation study.

关键词： Semantic segmentation Frequency-domain analysis Urban planning Semantics Speech enhancement Signal processing Transformers Sensors Remote sensing Standards

来源：评论

学校读者我要写书评

暂无评论

Automated analysis of continuum fields from atomistic simulations using statistical machine learning

arXiv

引用

arXiv 2022年

作者： Prakash, Arun Sandfeld, S. Institute of Mechancis and Fluid Dynamics Technical University Bergakademie Freiberg Lampadiusstraße 4 Freiberg09599 Germany Institute for Advanced Simulation – IAS-9: Materials Data Science and Informatics Forschungszentrum Juelich GmbH Juelich52425 Germany Chair of Materials Data Science and Materials Informatics Faculty 5 – Georesources and Materials Engineering RWTH Aachen University Aachen52056 Germany

Atomistic simulations of the molecular dynamics/statics kind are regularly used to study small scale plasticity. Contemporary simulations are performed with tens to hundreds of millions of atoms, with snapshots of these configurations written out at regular intervals for further analysis. Continuum scale constitutive models for material behavior can benefit from information on the atomic scale, in particular in terms of the deformation mechanisms, the accommodation of the total strain and partitioning of stress and strain fields in individual grains. In this work we develop a methodology using statistical data mining and machine learning algorithms to automate the analysis of continuum field variables in atomistic simulations. We focus on three important field variables: total strain, elastic strain and microrotation. Our results show that the elastic strain in individual grains exhibits a unimodal log-normal distribution, whilst the total strain and microrotation fields evidence a multimodal distribution. The peaks in the distribution of total strain are identified with a Gaussian mixture model and methods to circumvent overfitting problems are presented. Subsequently, we evaluate the identified peaks in terms of deformation mechanisms in a grain, which e.g., helps to quantify the strain for which individual deformation mechanisms are responsible. The overall statistics of the distributions over all grains are an important input for higher scale models, which ultimately also helps to be able to quantitatively discuss the implications for information transfer to phenomenological models. © 2022, CC BY-NC-SA.

关键词： Machine learning

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：