检索结果-内蒙古大学图书馆

Hopping-Transport Mechanism for Reconfigurable Logic in Disordered Dopant Networks

Physical Review Applied 2022年第6期17卷 064025-064025页

作者： Henri Tertilt Jesse Bakker Marlon Becker Bram de Wilde Indrek Klanberg Bernard J. Geurts Wilfred G. van der Wiel Andreas Heuer Peter A. Bobbert Institut für physikalische Chemie Westfälische Wilhelms-Universität Münster Münster Germany NanoElectronics Group University of Twente Enschede Netherlands Multiscale Modeling and Simulation Group Faculty of Electrical Engineering Mathematics and Computer Science MESA+ Institute for Nanotechnology and Center for Brain-Inspired Nano Systems (BRAINS) University of Twente Enschede Netherlands Center for Computational Energy Research (CCER) Eindhoven University of Technology Netherlands Fachbereich Physik Westfälische Wilhelms-Universität Münster Münster Germany Department of Applied Physics Eindhoven University of Technology Netherlands

We present an atomic-scale mechanism based on variable-range hopping of interacting charges enabling reconfigurable logic in dopant network processing units. Kinetic Monte Carlo simulations of the hopping process show temperature-dependent current-voltage characteristics and artificially evolved basic Boolean logic gates in very good agreement with experiment. The simulations provide unique insights into the local electrostatic potential and current flow in the dopant network, showing subtle changes induced by control voltages that set the conditions for the logic operation. These insights will be crucial in the systematic further development of this burgeoning technology for unconventional computing. The establishment of the principles underlying the logic functionality of these devices encourages the exploration and utilization of the same principles in other materials and device geometries.

关键词： Doping effects Hopping transport Neuromorphic computing Devices for digital logic, storage & processing Disordered systems Doped semiconductors Genetic algorithm Monte Carlo methods

来源：评论

学校读者我要写书评

暂无评论

AB1556 MACHINE LEARNING APPROACH FOR CLASSIFICATION OF ARTHRITIS ACTIVITY STATE, USING DATA FROM A SINGLE ACCELEROMETER

引用

Annals of the Rheumatic Diseases 2023年 82卷 2012-2012页

作者： P. Mielnik A. Myhre Hjelle A. Traseth K. Tokarz B. Pollen M. Fojcik Helse Forde Section for Rheumatology； Department for Neurology Rheumatology and Physical Medicine Forde Norway Institute of Informatics Silesian University of Technology Department of Graphics Computer Vision and Digital Systems Gliwice Poland Western Norway University of Applied Sciences Faculty of Engineering and Science Forde Norway

Background Machine learning (ML) potential is not fully exploited in diagnostics and follow up of autoimmune inflammatory rheumatic disease (AIIRD). It is despite the broader use of ML in, e.g. imaging diagnostics. The specific tools for AIIRD are lacking. Objectives This is an interim analysis of data from the first checkpoint of the proof-of-concept study on using accelerometer (ACC) data in follow up of patients with AIIRD. The main goal of the study was to investigate the value of single ACC data in the classification of arthritis activity status. Methods Subjects with AIIRD are enrolled in the study when they start of new treatment due to the disease activity. Several comorbidities, such as severe neurological and cardiological disorders, as wells as impaired mobility, are part of the protocol exclusion criteria. Volunteers without AIIRD who fulfil other inclusion/exclusion criteria are included as controls. The study was approved by local competent authorities and informed consent was given by all participants. We used data collected up till the first study checkpoint. This analysis covers nine patients with AIIRD (5 rheumatoid and four psoriasis arthritis) and 13 controls. Five patients had 3 study visits, 1 had 2 visits and 3 only one. Controls have only one visit per protocol. We analysed ACC data from 3 minutes of clapping using a home-brewed device based on Arduino nano 33 BLE with 6-axis MTU by Nordic Semiconductor. Data was divided into 6-second chunks that were found optimal in our prior study. We conducted binary classification between any/ no arthritis in any of the upper extremities. We used accuracy and area under curve (AUC) as an efficacy function derived from the receiver operating characteristic curve (ROC). We extracted 54 features from 3 ACC axis signal. The features encompassed, among others, Fourier's components, autoregression coefficients, median absolute deviation (MAD), variance, Fourier's entropy, etc. We built linear discriminant anal

关键词：

来源：评论

学校读者我要写书评

暂无评论

Semi-supervised Detector Training with Prototypes for Vehicle Detection

Semi-supervised Detector Training with Prototypes for Vehicl...

引用

International Conference on Intelligent Transportation

作者： Georg Waltner Michael Opitz Georg Krispel Horst Possegger Horst Bischof Institute of Computer Graphics and Vision (ICG) Faculty of Computer Science Graz University of Technology Graz Austria

Adapting detectors to new datasets is needed in scenarios where a user has a specific dataset that contains novel classes or is recorded in a setting where a pretrained detector fails. While detectors based on Convolutional Neural Networks (CNNs) are state-of-the-art and nowadays publicly available, they suffer from bad generalization capabilities when applied on datasets that notably differ from the one they were trained on. Finetuning the detector is only possible if the dataset is large enough to not destroy the underlying feature representation. We propose a method where only a few prototypes are labeled for training in a semi-supervised manner. In particular, we separate the detection from the classification step to avoid impairing the bounding box proposal generation. Our trained prototype classification network provides labels to automatically source a large dataset containing 20 to 30 times more samples without further supervision, which we then use to train a more powerful network. We evaluate our method on a private vehicle dataset with six classes and show that evaluating on a previously unseen recording site we can gain an accuracy increase of 9% at same precision and recall levels. We further show that finetuning with as few as 25 labeled samples per class doubles accuracy compared to directly using pretrained features for nearest neighbor classification.

关键词： Detectors Proposals Prototypes Training Object detection Feature extraction Task analysis

来源：评论

学校读者我要写书评

暂无评论

Task Allocation in Multi-agent Systems Using Many-objective Evolutionary Algorithm NSGA-III 4th

Task Allocation in Multi-agent Systems Using Many-objective ...

引用

4th International Conference on Machine Learning and Intelligent Communications, MLICOM 2019

作者： Zhou, Jing Zhao, Xiaozhe Zhao, Dongdong Lin, Zhong Faculty of Management and Economics Dalian University of Technology Dalian China Operation Software and Simulation Institute Dalian Naval Academy Dalian China School of Computer Science and Technology Wuhan University of Technology Wuhan China

ISBN: (纸本)9783030323875

Task allocation is an important issue in multi-agent systems, and finding the optimal solution of task allocation has been demonstrated to be an NP-hard problem. In many scenarios, agents are equipped with not only communication resources but also computing resources, so that tasks can be allocated and executed more efficiently in a distributed and parallel manner. Presently, many methods have been proposed for distributed task allocation in multi-agent systems. Most of them are either based on complete/full search or local search, and the former usually can find the optimal solutions but requires high computational cost and communication cost;the latter is usually more efficient but could not guarantee the solution quality. Evolutionary algorithm (EA) is a promising optimization algorithm which could be more efficient than the full search algorithms and might have better search ability than the local search algorithms, but it is rarely applied to distributed task allocation in multi-agent systems. In this paper, we propose a distributed task allocation method based on EA. We choose the many-objective EA called NSGA-III to optimize four objectives (i.e., maximizing the number of successfully allocated and executed tasks, maximizing the gain by executing tasks, minimizing the resource cost, and minimizing the time cost) simultaneously. Experimental results show the effectiveness of the proposed method, and compared with the full search strategy, the proposed method could solve task allocation problems with more agents and tasks. © 2019, ICST institute for computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Multi agent systems

来源：评论

学校读者我要写书评

暂无评论

Cross-Domain conditional generative adversarial networks for stereoscopic hyperrealism in surgical training

arXiv

引用

arXiv 2019年

作者： Engelhardt, Sandy Sharan, Lalith Karck, Matthias Simone, Raaele De Wolf, Ivo Faculty of Computer Science Mannheim University of Applied Sciences Department of Cardiac Surgery Heidelberg University Hospital Dep. of Simulation and Graphics Magdeburg University

Phantoms for surgical training are able to mimic cutting and suturing properties and patient-individual shape of organs, but lack a realistic visual appearance that captures the heterogeneity of surgical scenes. In order to overcome this in endoscopic approaches, hyperreal- istic concepts have been proposed to be used in an augmented reality- setting, which are based on deep image-to-image transformation meth- ods. Such concepts are able to generate realistic representations of phan- toms learned from real intraoperative endoscopic sequences. Conditioned on frames from the surgical training process, the learned models are able to generate impressive results by transforming unrealistic parts of the image (e.g. the uniform phantom texture is replaced by the more hetero- geneous texture of the tissue). Image-to-image synthesis usually learns a mapping G : X → Y such that the distribution of images from G(X) is indistinguishable from the distribution Y . However, it does not necessarily force the generated images to be consistent and without ar- tifacts. In the endoscopic image domain this can affect depth cues and stereo consistency of a stereo image pair, which ultimately impairs surgi- cal vision. We propose a cross-domain conditional generative adversarial network approach (GAN) that aims to generate more consistent stereo pairs. The results show substantial improvements in depth perception and realism evaluated by 3 domain experts and 3 medical students on a 3D monitor over the baseline method. In 84 of 90 instances our proposed method was preferred or rated equal to the baseline. Copyright © 2019, The Authors. All rights reserved.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

The Future of Fighter Pilot Training? Live Virtual Constructive in Large Force Exercises: Perceived and Expected Training Value

引用

The International Journal of Aviation Psychology 2024年第1期34卷

作者： Rogier Woltjer Robert Ramberg Henrik Artman Sanna Aronsson Mikael Mitchell Per-Anders Oskarsson a Swedish Air Force Combat Simulation Center FLSC Swedish Defence Research Agency FOI Stockholm Sweden a Swedish Air Force Combat Simulation Center FLSC Swedish Defence Research Agency FOI Stockholm Swedenb Department of Computer and Systems Sciences Faculty of Social Sciences Stockholm University Stockholm Sweden a Swedish Air Force Combat Simulation Center FLSC Swedish Defence Research Agency FOI Stockholm Swedenc Division of Media Technology and Interaction Design School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden a Swedish Air Force Combat Simulation Center FLSC Swedish Defence Research Agency FOI Stockholm Swedend 221 Sqn Swedish Air Force Luleå Sweden e Department of Man-Technology-Organisation Swedish Defence Research Agency FOI Linköping Sweden

ABSTRACTObjectiveThis study investigates whether and how perceived training value varies among fighter pilots participating in a large force exercise (LFE), and whether they expect Live Virtual Constructive (LVC) training to enhance training value of future *** technology is maturing but its expected training value is under-investigated, especially regarding future LVC in *** pilots at a Live-flying LFE answered questionnaires, 120 responses were analyzed statistically. The questions focused on whether the sortie they just flew provided training value, generally and for specific Desired Learning Objectives (DLOs), on whether the inclusion of Virtual and Constructive entities in future LFEs would contribute to training value generally and to the DLOs specifically, and on specific LVC implementation *** of experienced training value varied across DLOs, and DLO ratings varied across Mission types. Fighter pilots were positive toward including Virtual and Constructive entities in LFEs for certain DLOs, mostly for flying a complex mission. DLOs covaried around the factors of LVC-tradeoff advantages and Live advantages, as well as temporal dimensions of planning before a mission and stages of engagement. LVC implementation lessons were *** pilots are generally moderately positive about LVC in LFEs. The measurement of perceived training value per DLO seems a relevant instrument for investigating perceived training value of an LFE and expected training value of future LVC training.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Depth-Awareness in a System for Mixed-Reality Aided Surgical Procedures 15th

Depth-Awareness in a System for Mixed-Reality Aided Surgical...

引用

15th International Conference on Intelligent Computing, ICIC 2019

作者： Sylos Labini, Mauro Gsaxner, Christina Pepe, Antonio Wallner, Juergen Egger, Jan Bevilacqua, Vitoantonio Institute for Computer Graphics and Vision Faculty of Computer Science and Biomedical Engineering Graz University of Technology Graz Austria Computer Algorithms for Medicine Laboratory Graz Austria Department of Electrical and Information Engineering Polytechnic University of Bari Bari Italy Department of Oral and Maxillofacial Surgery Medical University of Graz Graz Austria

ISBN: (纸本)9783030267650

computer-assisted surgery is a trending topic in research, with many different approaches which aim at supporting surgeons in the operating room. Existing surgical planning and navigation solutions are often considered to be distracting, unintuitive or hard to interpret. In this work, we address this issue with an approach based on mixed reality devices like Microsoft HoloLens. We assess the depth sensing capabilities of Microsoft HoloLens, and the potential benefit they could bring to computer-assisted surgery applications. © 2019, Springer Nature Switzerland AG.

关键词： Mixed reality

来源：评论

学校读者我要写书评

暂无评论

Distributed Coordinated Emergency Frequency Control Strategy for Renewables-Rich Power Systems Based on Multi-Agent Reinforcement Learning

SSRN

引用

SSRN 2025年

作者： Sun, Zhenglong Han, Junpeng Chen, Weihan Zhang, Rui Liu, Yong Levron, Yoash Belikov, Juri Department of Electrical Engineering Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin China The Andrew and Erna Viterbi Faculty of Electrical & Computer Engineering Technion—Israel Institute of Technology Haifa3200003 Israel Department of Software Science Tallinn University of Technology Akadeemia Tee 15a Tallinn12618 Estonia

With the rapid growth of renewable energy in the power system and the implementation of large-capacity LCC-HVDC projects, the frequency support capability of the grid has been reduced. Simultaneously, the renewables-rich grid faces the risk of high-power shocks due to LCC-HVDC blocking. Existing centralized, single-measure approaches and the model-based emergency frequency control (EFC) methods are facing tremendous challenges. To fill this gap, this paper proposes a frequency emergency control method for renewables-rich grids that coordinates LCC-HVDC emergency power support and load shedding. The coordination of the two stabilization measures is modeled as a decentralized partially observable Markov decision process, and a multi-agent reinforcement learning (MADRL) approach is employed to optimize the power of measures for each stabilization control substation. An MADRL algorithm that integrates the system frequency response model is developed to overcome the problem of inaccurate evaluation of centralized action value in the MADRL algorithm used for EFC. This approach allows for the global optimization of the distributed coordination strategy through centralized training. The effectiveness and adaptability of the proposed method are demonstrated through simulations of the modified IEEE 39-bus system. © 2025, The Authors. All rights reserved.

关键词： Deep reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

The impact of AI on engineering design procedures for dynamical systems

arXiv

引用

arXiv 2024年

作者： de Payrebrune, Kristin M. Flaßkamp, Kathrin Ströhla, Tom Sattel, Thomas Bestle, Dieter Röder, Benedict Eberhard, Peter Peitz, Sebastian Stoffel, Marcus Rutwik, Gulakala Aditya, Borse Wohlleben, Meike Sextro, Walter Raff, Maximilian Remy, C. David Yadav, Manish Stender, Merten van Delden, Jan Lüddecke, Timo Langer, Sabine C. Schultz, Julius Blech, Christopher RPTU Kaiserslautern-Landau Institute for Computational Physics in Engneering Gottlieb-Daimler-Straße 74 Kaiserslautern67663 Germany Saarland University Systems Engineering Systems Modeling and Simulation Campus Saarbrücken66123 Germany TU Ilmenau Mechatronics group Max-Planck-Ring 12 Ilmenau98693 Germany Brandenburg University of Technology Cottbus-Senftenberg Chair of Engineering Mechanics and Vehicle Dynamics Siemens-Halske-Ring 14 Cottbus03046 Germany University of Stuttgart Institute of Engineering and Computational Mechanics Pfaffenwaldring 9 Stuttgart70569 Germany TU Dortmund Department of Computer Science & Lamarr Institute Joseph-von-Fraunhofer-Str. 25 Dortmund44227 Germany RWTH Aachen University Institute of General Mechanics Eilfschornsteinstraße 18 Aachen52062 Germany Paderborn University Faculty of Mechanical Engineering Dynamics and Mechatronics Warburger Str. 100 Paderborn33098 Germany University of Stuttgart Institute for Nonlinear Mechanics Pfaffenwaldring 9 Stuttgart70569 Germany Technische Universität Berlin Chair of Cyber-Physical Systems in Mechanical Engineering Straße des 17. Juni 135 Berlin10623 Germany University of Göttingen Institute of Informatics Goldschmidtstr. 7 Göttingen37077 Germany Technische Universität Braunschweig Institute for Acoustics and Dynamics Langer Kamp 19 Braunschweig38106 Germany

Artificial intelligence (AI) is driving transformative changes across numerous fields, revolutionizing conventional processes and creating new opportunities for innovation. The development of mechatronic systems is undergoing a similar transformation. Over the past decade, modeling, simulation, and optimization techniques have become integral to the design process, paving the way for the adoption of AI-based methods. In this paper, we examine the potential for integrating AI into the engineering design process, using the V-model from the VDI guideline 2206, considered the state-of-the-art in product design, as a foundation. We identify and classify AI methods based on their suitability for specific stages within the engineering product design workflow. Furthermore, we present a series of application examples where AI-assisted design has been successfully implemented by the authors. These examples, drawn from research projects within the DFG Priority Program SPP 2353: Daring More Intelligence - Design Assistants in Mechanics and Dynamics, showcase a diverse range of applications across mechanics and mechatronics, including areas such as acoustics and robotics. © 2024, CC BY.

关键词： Mechatronics

来源：评论

学校读者我要写书评

暂无评论

Comparison of MRI measurements and CFD simulations of hemodynamics in intracranial aneurysms using a 3D printed model Influence of noisy MRI measurements

Comparison of MRI measurements and CFD simulations of hemody...

引用

90th Annual Meeting of the International Association of Applied Mathematics and Mechanics, GAMM 2019

作者： Giese, Daniel Heinlein, Alexander Klawonn, Axel Knepper, Jascha Sonnabend, Kristina University of Cologne Faculty of Medicine and University Hospital Cologne Institute for Diagnostic and Interventional Radiology Cologne Germany University of Cologne Department of Mathematics and Computer Science Cologne Germany University of Cologne Center for Data and Simulation Science Cologne Germany

MRI (Magnetic Resonance Imaging) measurements and CFD (Computational Fluid Dynamics) simulations for blood flow in intracranial aneurysms are compared for a benchmark problem. In particular, it is shown that noise and other artifacts in the MRI measurements have an influence on certain properties of the flow field, e.g., on the boundary flow and mass conservation. © 2019 The Authors Proceedings in Applied Mathematics & Mechanics published by Wiley-VCH Verlag GmbH & Co. KGaA Weinheim.

关键词： Magnetic resonance imaging

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：