检索结果-内蒙古大学图书馆

arXiv 2019年

作者： Wally, Bernhard Vyskočil, Jiří Novák, Petr Huemer, Christian Šindelář, Radek Kadera, Petr Mazak, Alexandra Wimmer, Manuel CDL for Model-Integrated Smart Production Department of Business Informatics Software Engineering Johannes Kepler University Linz Linz4040 Austria Department of Intelligent Systems for Industry and Smart Distribution Networks Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Prague160 00 Czech Republic Business Informatics Group Institute of Information Systems Engineering Technische Universitat Wien Vienna1040 Austria

Model-driven engineering (MDE) provides tools and methods for the manipulation of formal models. In this letter, we leverage MDE for the transformation of production system models into flat files that are understood by general purpose planning tools and that enable the computation of "plans", i.e., sequences of production steps that are required to reach certain production goals. These plans are then merged back into the production system model, thus enriching the formalized production system knowledge. Copyright © 2019, The Authors. All rights reserved.

关键词： Factory automation

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Spatio-Temporal Road Scene Reconstruction Using Superpixel Markov Random Field

arXiv

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arXiv 2018年

作者： Li, Yaochen Liu, Yuehu Zhu, Jihua Ma, Shiqi Niu, Zhenning Guo, Rui School of Software Engineering Xi’an Jiaotong University Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University School of Information and Electronics Beijing Institute of Technology

Scene model construction based on image rendering is an indispensable but challenging technique in computer vision and intelligent transportation systems. In this paper, we propose a framework for constructing 3D corridor-based road scene models. This consists of two successive stages: road detection and scene construction. The road detection is realized by a new superpixel Markov random field (MRF) algorithm. The data fidelity term in the MRF’s energy function is jointly computed according to the superpixel features of color, texture and location. The smoothness term is established on the basis of the interaction of spatio-temporally adjacent superpixels. In the subsequent scene construction, the foreground and background regions are modeled independently. Experiments for road detection demonstrate the proposed method outperforms the state-of-the-art in both accuracy and speed. The scene construction experiments confirm that the proposed scene models show better correctness ratios, and have the potential to support a range of applications. Copyright © 2018, The Authors. All rights reserved.

关键词： Textures

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3D Traffic Scenes Construction and Simulation based on Scene Stages

3D Traffic Scenes Construction and Simulation based on Scene...

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Chinese Automation Congress

作者： Jian Yuan Yaochen Li Huayun Pan Zhichao Cui Yuehu Liu School of Software Engineering Xi'an Jiaotong University Xi'an China Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an China

In this paper, a method is presented for 3D traffic Scenes Construction and Simulation based on scene stages. The construction method is divided into two phases: scene stages analysis phase and scene model construction phase. During the analysis phase of the scene stages, the content of the road image is analyzed by using semantic labels, then we define road scene elements based on the graph models. Furthermore, the wireframe models of traffic scenes are constructed. During the phrase of scene model construction, the scene layout is firstly displayed based on the Scene Stages, and then the road scene model of the 3D corridor structure is quickly constructed. The applications for road scene simulation are then developed, which prove the effectiveness of the proposed framework.

关键词： Scene stage Scene construction Semantic label Graph model

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Mars helicopter technology demonstrator

Mars helicopter technology demonstrator

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AIAA Atmospheric Flight Mechanics Conference, 2018

作者： Bob Balaram, J. Canham, Timothy Duncan, Courtney Golombek, Matt Grip, Håvard Fjær Johnson, Wayne Maki, Justin Quon, Amelia Stern, Ryan Zhu, David Jet Propulsion Laboratory California Institute of Technology PasadenaCA91109 United States NASA Ames Research Center Moffet Field CA94035 United States Robotics Technologist JPL Mobility and Robotic Systems Section JPL Flight Software and Avionics Systems Section JPL Flight Communications Systems Section JPL Planetary Science Section Robotics Technologist JPL Guidance and Control Section NASA Ames Aeromechanics Office JPL Instrument Software and Science Data Systems Section JPL Spacecraft Mechanical Engineering Section JPL Flight Electronics Section Robotics Electrical Engineer JPL Mobility and Robotic Systems Section

We describe a helicopter that is being developed as a technology demonstrator of Mars aerial mobility. The key design features of the helicopter, associated test infrastructure, and results from a full-scale prototype... 详细信息

ISBN: (数字)9781624105258

ISBN: (纸本)9781624105258

关键词： Antennas

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MedShapeNet - A Large-Scale Dataset of 3D Medical Shapes for Computer Vision

arXiv

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arXiv 2023年

作者： Li, Jianning Zhou, Zongwei Yang, Jiancheng Pepe, Antonio Gsaxner, Christina Luijten, Gijs Qu, Chongyu Zhang, Tiezheng Chen, Xiaoxi Li, Wenxuan Wodzinski, Marek Friedrich, Paul Xie, Kangxian Jin, Yuan Ambigapathy, Narmada Nasca, Enrico Solak, Naida Melito, Gian Marco Vu, Viet Duc Memon, Afaque R. Schlachta, Christopher De Ribaupierre, Sandrine Patel, Rajnikant Eagleson, Roy Chen, Xiaojun Mächler, Heinrich Kirschke, Jan Stefan de la Rosa, Ezequiel Christ, Patrick Ferdinand Li, Hongwei Bran Ellis, David G. Aizenberg, Michele R. Gatidis, Sergios Küstner, Thomas Shusharina, Nadya Heller, Nicholas Andrearczyk, Vincent Depeursinge, Adrien Hatt, Mathieu Sekuboyina, Anjany Löffler, Maximilian Liebl, Hans Dorent, Reuben Vercauteren, Tom Shapey, Jonathan Kujawa, Aaron Cornelissen, Stefan Langenhuizen, Patrick Ben-Hamadou, Achraf Rekik, Ahmed Pujades, Sergi Boyer, Edmond Bolelli, Federico Grana, Costantino Lumetti, Luca Salehi, Hamidreza Ma, Jun Zhang, Yao Gharleghi, Ramtin Beier, Susann Sowmya, Arcot Garza-Villarreal, Eduardo A. Balducci, Thania Angeles-Valdez, Diego Souza, Roberto Rittner, Leticia Frayne, Richard Ji, Yuanfeng Ferrari, Vincenzo Chatterjee, Soumick Dubost, Florian Schreiber, Stefanie Mattern, Hendrik Speck, Oliver Haehn, Daniel John, Christoph Nürnberger, Andreas Pedrosa, João Ferreira, Carlos Aresta, Guilherme Cunha, António Campilho, Aurélio Suter, Yannick Garcia, Jose Lalande, Alain Vandenbossche, Vicky Van Oevelen, Aline Duquesne, Kate Mekhzoum, Hamza Vandemeulebroucke, Jef Audenaert, Emmanuel Krebs, Claudia van Leeuwen, Timo Vereecke, Evie Heidemeyer, Hauke Röhrig, Rainer Hölzle, Frank Badeli, Vahid Krieger, Kathrin Gunzer, Matthias Chen, Jianxu van Meegdenburg, Timo Dada, Amin Balzer, Miriam Fragemann, Jana Jonske, Frederic Rempe, Moritz Malorodov, Stanislav Bahnsen, Fin H. Seibold, Constantin Jaus, Alexander Marinov, Zdravko Jaeger, Paul F. Stiefelhagen, Rainer Santos, Ana Sofia Lindo, Mariana Ferreira, André Alves, Victor Kamp, Michael Abourayya, Amr Nensa, Felix Hörst, Fabian Brehmer, Alexander Hein Girardetstraße 2 Essen45131 Germany Graz University of Technology Inffeldgasse 16c Graz8010 Austria Graz Austria The Department of Computer Science Johns Hopkins University Malone Hall 3400 N Charles St BaltimoreMD21218 United States Rte Cantonale Lausanne1015 Switzerland The Department of Measurement and Electronics AGH University of Science and Technology Krakow Poland Sierre Switzerland Department of Biomedical Engineering University of Basel Hegenheimermattweg Allschwil167C CH-4123 Switzerland The Boston College 140 Commonwealth Ave Chestnut HillMA02467 United States University Hospital B7-200 339 Windermere Road LondonN6A 5A5 Canada The Department of Radiology Renji Hospital School of Medicine Shanghai Jiao Tong University Shanghai200240 China The Research Center for Connected Healthcare Big Data ZhejiangLab Zhejiang Hangzhou311121 China The Faculty of Statistics Technical University Dortmund August-Schmidt-Straße 1 Dortmund44227 Germany The Institute of Mechanics Graz University of Technology Kopernikusgasse 24/IV Graz8010 Austria The Institute of Biomedical Manufacturing and Life Quality Engineering State Key Laboratory of Mechanical System and Vibration School of Mechanical Engineering Shanghai Jiao Tong University 800 Dongchuan Road Shanghai200240 China The Department of Cardiac Surgery Medical University Graz Auenbruggerplatz 29 Graz8036 Austria The Department of Diagnostic and Interventional Radiology University Hospital Giessen Justus-Liebig-University Giessen Klinikstraße 33 Giessen35392 Germany The Department of Mechanical Engineering Mehran University of Engineering and Technology Jamshoro Sindh76062 Pakistan The Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China The Geschäftsführender Oberarzt Abteilung für Interventionelle und Diagnostische Neuroradiologie Universitätsklinikum der Technischen Universität München Ismaningerstr. 22 München81675 Germany icometrix Kolonel Be

Prior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from numerous shape-related publications in premier vision conferences as well as the growing popularity of ShapeNet (about 51,300 models) and Princeton ModelNet (127,915 models). For the medical domain, we present a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems. As a unique feature, we directly model the majority of shapes on the imaging data of real patients. As of today, MedShapeNet includes 23 datasets with more than 100,000 shapes that are paired with annotations (ground truth). Our data is freely accessible via a web interface and a Python application programming interface (API) and can be used for discriminative, reconstructive, and variational benchmarks as well as various applications in virtual, augmented, or mixed reality, and 3D printing. Exemplary, we present use cases in the fields of classification of brain tumors, skull reconstructions, multi-class anatomy completion, education, and 3D printing. In future, we will extend the data and improve the interfaces. The project pages are: https://***/ and https://***/Jianningli/*** Codes 68T01 © 2023, CC BY.

关键词： Large dataset

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Traffic Sensory Data Classification by Quantifying Scenario Complexity

Traffic Sensory Data Classification by Quantifying Scenario ...

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IEEE Symposium on Intelligent Vehicle

作者： Jiajie Wang Chi Zhang Yuehu Liu Qilin Zhang school of Software Engineering Xi’an Jiaotong University Xi’an China Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an China Faculty of the Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an China HERE Technologies Chicago Illinois USA

ISBN: (纸本)9781538644539

For unmanned ground vehicle (UGV) off-line testing and performance evaluation, massive amount of traffic scenario data is often required. The annotations in current off-line traffic sensory dataset typically include I) types of roadways II) scene types III) specific characteristics that are generally considered challenging for cognitive algorithms. While such annotations are helpful in manual selection of data, they are insufficient for comprehensive and quantitate measurement of per-roadway-segment scenario complexity. To resolve such limitations, we propose a traffic sensory data classification paradigm based on quantifying the scenario complexity for each roadway segment, where such quantification is jointly based on road semantic complexity and traffic element complexity. The road semantic complexity is a proposed measurement of the complexity incurred by the static elements such as curvy roads, intersections, merges and splits, which is predicted with a Support Vector Regression (SVR). The traffic element complexity is a measurement of complexity due to dynamic traffic elements, such as nearby vehicles and pedestrians. Experimental results and a case study verify the efficacy of the proposed method.

关键词： Complexity theory Roads Semantics Testing Robot sensing systems Unmanned vehicles

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Joint learning of discriminative low-dimensional image representations based on dictionary learning and two-layer orthogonal projections

arXiv

引用

arXiv 2019年

作者： Wei, Xian Shen, Hao Li, Yuanxiang Tang, Xuan Jin, Bo Zhao, Lijun Murphey, Yi Lu Fujian Institute of Research on the Structure of Matter Chinese Academy of Sciences China Technical University of Munich Germany and fortiss GmbH Munich Germany Shanghai Key Lab for Trustworthy Computing School of Computer Science and Software Engineering East China Normal University China School of Aeronautics & Astronautics Shanghai Jiao Tong University Shanghai200240 China State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin150006 China Department of Electrical and Computer Engineering University of Michigan-Dearborn DearbornMI48128 United States

This work investigates the problem of efficiently learning discriminative low-dimensional representations of multi-class large-scale image objects. We propose a generic deep learning approach by taking advantages of Convolutional Neural Networks (CNN), sparse dictionary learning, and orthogonal projections. CNN is not only powerful on feature extraction, but also robust to spatial variance and changes. Sparse dictionary learning is well known for disentangling nonlinear underlying discriminative factors in data. The orthogonal projection is a notable efficient tool to project multi-class data onto low dimensional discriminative subspace. The proposed procedure can be summarized as follows. At first, a CNN is employed to extract high-dimensional (HD) preliminary convolutional features. Secondly, to avoid the high computational cost of direct sparse coding on HD CNN features, we present to learn sparse representation (SR) in an orthogonal projected space over a task-driven sparsifying dictionary. We then exploit the discriminative projection on SR. The whole learning process is treated as a joint optimization problem of trace quotient maximization, which involves the CNN parameters, the orthogonal projection on CNN features, the dictionary and the discriminative projection on sparse codes. The related cost function is well defined on product manifold of the Stiefel manifold, the Oblique manifold, and the Grassmann manifold. It is optimized via a geometrical stochastic gradient descent algorithm. Finally, the quality of dictionary and projections learned by the proposed approach is further investigated in terms of image classification. The experimental results show that the approach can achieve a strongly competitive performance with state-of-the-art image classification methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Convolution

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A Scalable Deep Attention Mechanism of Instance Segmentation for the Investigation of Chromosome

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SLAS technology 2025年 33卷 100306页

作者： Neelam Umbreen Sara Ali Hasan Sajid Yasar Ayaz Shrooq Alsenan Yunyoung Nam So Yeon Kim Muhammad Baber Sial National University of Sciences and Technology (NUST) Islamabad Pakistan. Electronic address: neelamumbreen.phd@smme.edu.pk. National University of Sciences and Technology (NUST) Islamabad Pakistan Suzhou Industrial Park (SIP) Monash Research Institute of Science and Technology Monash University 8Q9C+R6W Suzhou Jiangsu China. School of Mechanical and Manufacturing Engineering (SMME) National University of Sciences and Technology (NUST) Islamabad NUST-COVENTRY Human-Robot Interaction (NC-HRI) Laboratory Pakistan Intelligent Field Robotics Laboratory (IFRL) National Center for Artificial Intelligence (NCAI) National University of Sciences and Technology (NUST) Islamabad Pakistan. Electronic address: sarababer@smme.nust.edu.pk. National University of Sciences and Technology (NUST) Islamabad Pakistan. Electronic address: hasan.sajid@smme.nust.edu.pk. Intelligent Field Robotics Laboratory (IFRL) National Center for Artificial Intelligence (NCAI) National University of Sciences and Technology (NUST) Islamabad Pakistan. Electronic address: yasar@smme.nust.edu.pk. Information Systems Department College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia. Electronic address: shalsenan@pnu.edu.sa. Emotional and Intelligent Child Care Convergence Research Center Soonchunhyang University Asan 31538 Korea. Electronic address: ynam@sch.ac.kr. Department of Artificial Intelligence Department of Software and Computer Engineering Ajou University. Electronic address: jebi1771@ajou.ac.kr. School of Automation Science and Electrical Engineering Beihang University Beijing 100191 China. Electronic address: babersial@***.

Chromosome segmentation in metaphase images is a critical yet challenging task in cytogenetics and genomics due to the inherent complexity, variability in chromosome shapes, and the scarcity of high-quality annotated datasets. This study proposes a robust instance segmentation framework that integrates an automated annotation pipeline with an enhanced deep learning architecture to address these challenges. A novel dataset is introduced, comprising metaphase images and corresponding karyograms, annotated with precise instance segmentation information across 24 chromosome classes in COCO format. To overcome the labor-intensive manual annotation process, a feature-based image registration technique leveraging SIFT and homography is employed, enabling the accurate mapping of chromosomes from karyograms to metaphase images and significantly improving annotation quality and segmentation performance. The proposed framework includes a custom Mask R-CNN model enhanced with an Attention-based Feature Pyramid Network (AttFPN), spatial attention mechanisms, and a LastLevelMaxPool block for superior multi-scale feature extraction and focused attention on critical regions of the image. Experimental evaluations demonstrate the model's efficacy, achieving a mean average precision (mAP) of 0.579 at IoU = 0.50:0.95, surpassing the baseline Mask R-CNN and Mask R-CNN with AttFPN by 3.94% and 5.97% improvements in mAP and AP50, respectively. Notably, the proposed architecture excels in segmenting small and medium-sized chromosomes, addressing key limitations of existing methods. This research not only introduces a state-of-the-art segmentation framework but also provides a benchmark dataset, setting a new standard for chromosome instance segmentation in biomedical imaging. The integration of automated dataset creation with advanced model design offers a scalable and transferable solution, paving the way for tackling similar challenges in other domains of biomedical and cytogenetic imagi

关键词： Chromosome segmentation Instance segmentation, cytogenetics Mask R-CNN attention mechanism biomedical imaging multi-scale feature fusion

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Using Image Mapping to Analyze Moving Vehicle Weight on Bridges 7

Using Image Mapping to Analyze Moving Vehicle Weight on Brid...

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7th IEEE Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2017

作者： Zhan, Shaodong Chen, Meng Li, Zhi Zhang, Guanglie Institute of Intelligent Cyber Sensing System Shenzhen Academy of Robotics Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9781538604892

In this paper, we propose a novel Bridge Weight-In-Motion (B-WIM) detection method based on the bridge deck vibration signal analysis with the image mapping algorithm. The algorithm maps the vibration signals into 8-Bit grayscale images and then uses image sharpness to detect B-WIM in single-vehicle tests. The result shows that the proposed method can effectively estimate B-WIM. © 2017 IEEE.

关键词： Vibration analysis

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Machine learning–based detection model of early Alzheimer's disease using wearable device and gait assessment

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Alzheimer's & Dementia 2021年第S5期17卷

作者： YoungHoon Jeon Thi Kieu Khanh Ho Jaeyong Kang Byeong C. Kim Kun Ho Lee Jong-In Song Jeonghwan Gwak School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology Gwangju South Korea Department of Software Korea National University of Transportation Chungju South Korea Department of Neurology Chonnam National University Medical School Gwangju South Korea Department of Biomedical Science Chosun University Gwangju South Korea Aging Neuroscience Research Group Korea Brain Research Institute Daegu South Korea Gwangju Alzheimer’s Disease and Related Dementia Cohort Research Center Chosun University Gwangju South Korea Department of IT & Energy Convergence (BK21 FOUR) Korea National University of Transportation Chungju South Korea Department of AI Robotics Engineering Korea National University of Transportation Chungju South Korea Department of Biomedical Engineering Korea National University of Transportation Chungju South Korea

Background A method for diagnosing early stages of Alzheimer's disease (AD) is developed by analyzing human gait and training a machine learning classifier using signals obtained from inertial measurement unit. This patient-friendly and cost-effective method enables periodic monitoring, contributing to savings in treatment costs and effort. Method 145 subjects (77 healthy controls, 68 Mild Cognitive Impairment) were employed to perform seven types of gait paradigms, including normal and complex walking. The gait of the employed subjects was measured by 7 shimmer devices, which consist of a three-dimensional accelerometer and gyroscope. The acquired signal data is converted into 35 features including gait cycle. The extracted features are used to train an ensemble classifier based on machine learning with an oversampling method. In order to find the optimal combination in the ensemble model, an elimination technique was used to compare performance by removing single classifiers one by one. For performance comparison, the dataset is divided into training set/test set by leave-one-patient-out cross-validation, and several metrics such as accuracy and F1 score are used. Result The classifier for MCI detection classified severity to an accuracy of about 0.7 for 145 subjects. The combination of oversampling techniques and different single classifiers contributes to improved performance. In the seven walking experimental paradigms, the accuracy of the classification of normal walking without subtasks is about 0.67. O the other hand, the classification of walking with associative word-speaking or counting numbers shows significant results with accuracy of 0.7413 and 0.7338, respectively. Conclusion Our proposed method predicts severity through efficient walking with less space-time constraints. Subtasks that stress the brain in walking contribute to the performance improvement of the ensemble model with oversampling. These results demonstrate that our proposed method with g

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