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arXiv 2020年

作者： Li, Tongxin Sun, Bo Chen, Yue Ye, Zixin Low, Steven H. Wierman, Adam The Computing + Mathematical Sciences Department California Institute of Technology PasadenaCA91125 United States The Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong The Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong The Electrical Engineering Department California Institute of Technology PasadenaCA91125 United States

Aggregators have emerged as crucial tools for the coordination of distributed, controllable loads. To be used effectively, an aggregator must be able to communicate the available flexibility of the loads they control, as known as the aggregate flexibility to a system operator. However, most of existing aggregate flexibility measures often are slow-timescale estimations and much less attention has been paid to real-time coordination between an aggregator and an operator. In this paper, we consider solving an online optimization in a closed-loop system and present a design of real-time aggregate flexibility feedback, termed the maximum entropy feedback (MEF). In addition to deriving analytic properties of the MEF, combining learning and control, we show that it can be approximated using reinforcement learning and used as a penalty term in a novel control algorithm - the penalized predictive control (PPC), which modifies vanilla model predictive control (MPC). The benefits of our scheme are (1). Efficient Communication. An operator running PPC does not need to know the exact states and constraints of the loads, but only the MEF. (2). Fast Computation. The PPC often has much less number of variables than an MPC formulation. (3). Lower Costs. We show that under certain regularity assumptions, the PPC is optimal. We illustrate the efficacy of the PPC using a dataset from an adaptive electric vehicle charging network and show that PPC outperforms classical MPC. © 2020, CC BY.

关键词： Reinforcement learning

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Classification of Whole-Slide Pathology Images Based on State Space Models and Graph Neural Networks

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Electronics 2025年第10期14卷

作者： Feng Ding Chengfei Cai Jun Li Mingxin Liu Yiping Jiao Zhengcan Wu Jun Xu School of Automation Nanjing University of Information Science and Technology Nanjing 210044 China College of Information Engineering TaiZhou University Taizhou 225300 China Jiangsu Key Laboratory of Intelligent Medical Image Computing School of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing 210044 China Department of Radiology Nanjing First Hospital Nanjing Medical University Nanjing 211112 China

Whole-slide images (WSIs) pose significant analytical challenges due to their large data scale and complexity. Multiple instance learning (MIL) has emerged as an effective solution for WSI classification, but existing frameworks often lack flexibility in feature integration and underutilize sequential information. To address these limitations, this work proposes a novel MIL framework: Dynamic Graph and State Space Model-Based MIL (DG-SSM-MIL). DG-SSM-MIL combines graph neural networks and selective state space models, leveraging the former’s ability to extract local and spatial features and the latter’s advantage in comprehensively understanding long-sequence instances. This enhances the model’s performance in diverse instance classification, improves its capability to handle long-sequence data, and increases the precision and scalability of feature fusion. We propose the Dynamic Graph and State Space Model (DynGraph-SSM) module, which aggregates local and spatial information of image patches through directed graphs and learns global feature representations using the Mamba model. Additionally, the directed graph structure alleviates the unidirectional scanning limitation of Mamba and enhances its ability to process pathological images with dispersed lesion distributions. DG-SSM-MIL demonstrates superior performance in classification tasks compared to other models. We validate the effectiveness of the proposed method on features extracted from two pretrained models across four public medical image datasets: BRACS, TCGA-NSCLC, TCGA-RCC, and CAMELYON16. Experimental results demonstrate that DG-SSM-MIL consistently outperforms existing MIL methods across four public datasets. For example, when using ResNet-50 features, our model achieves the highest AUCs of 0.936, 0.785, 0.879, and 0.957 on TCGA-NSCLC, BRACS, CAMELYON16, and TCGA-RCC, respectively. Similarly, with UNI features, DG-SSM-MIL reaches AUCs of 0.968, 0.846, 0.993, and 0.990, surpassing all baselines. These re

关键词： Graph neural networks

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The design of a quasi-time optimal cascade controller for ball and beam system

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IOP Conference Series: Materials Science and engineering 2021年第1期1029卷

作者： C X Nguyen H Ng Phan L D Hoang H Ng Tran Department of Automation and Computing Techniques Le Quy Don Technical University Vietnam Department of Software Engineering Le Quy Don Technical University Hanoi Vietnam

In this paper, a methodology for designing quasi-time optimal cascade controller for ball and beam system is presented and the result is compared with LQR method as well as implemented on real system. Ball and beam system is a highly non-linear system, its parameters are difficult to estimate accurately and easily affect by disturbance. In designed method, a mathematical model describing the system is built, including a motor that creates a rotation of beam and is divided into two subsystems when synthesizing the cascade controller for the system. The first floor is the beam subsystem with the output is the angle as the set value for the second floor, which is ball subsystem. The controller is synthesized for each subsystem based on the quasi-time optimal control. The advantage of this method is the synthesizing control law with non-linear system. The simulation results show the effectiveness of the proposed design.

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A Self-Powered Nanogenerator for the Electrical Protection of Integrated Circuits from Trace Amounts of Liquid

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Nano-Micro Letters 2020年第1期12卷 54-62页

作者： Zhuang Hui Ming Xiao Daozhi Shen Jiayun Feng Peng Peng Yangai Liu Walter WDuley YNorman Zhou Beijing Key Laboratory of Materials Utilization of Nonmetallic Minerals and Solid Wastes National Laboratory of Mineral MaterialsSchool of Materials Science and TechnologyChina University of GeosciencesBeijing 100083People’s Republic of China Department of Mechanics and Mechatronics Engineering Centre for Advanced Materials JoiningUniversity of WaterlooWaterlooON N2L 3G1Canada School of Mechanical Engineering and Automation Beihang UniversityBeijing 100191People’s Republic of China Waterloo Institute of Nanotechnology University of WaterlooWaterlooON N2L 3G1Canada Institute for Quantum Computing University of WaterlooWaterlooON N2L 3G1Canada State Key Laboratory of Advanced Welding and Joining Harbin Institute of TechnologyHarbin 150001People’s Republic of China Department of Physics and Astronomy University of WaterlooWaterlooON N2L 3G1Canada

With the increase in the use of electronic devices in many different environments,a need has arisen for an easily implemented method for the rapid,sensitive detection of liquids in the vicinity of electronic *** this work,a high-performance power generator that combines carbon nanoparticles and TiO2 nanowires has been fabricated by sequential electrophoretic deposition(EPD).The opencircuit voltage and short-circuit current of a single generator are found to exceed 0.7 V and 100μA when 6μL of water was *** generator is also found to have a stable and reproducible response to other *** output voltage of 0.3 V was obtained after 244,876,931,and 184μs,on exposure of the generator to 6μL of water,ethanol,acetone,and methanol,*** fast response time and high sensitivity to liquids show that the device has great potential for the detection of small quantities of *** addition,the simple easily implemented sequential EPD method ensures the high mechanical strength of the *** compact,reliable device provides a new method for the sensitive,rapid detection of extraneous liquids before they can impact the performance of electronic circuits,particularly those on printed circuit board.

关键词： Self-powered generator Sub-millisecond response Liquid protection

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Localizing the Recurrent Laryngeal Nerve via Ultrasound with a Bayesian Shape Framework

arXiv

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arXiv 2022年

作者： Dou, Haoran Han, Luyi He, Yushuang Xu, Jun Ravikumar, Nishant Mann, Ritse Frangi, Alejandro F. Yap, Pew-Thian Huang, Yunzhi Institute for AI in Medicine School of Automation Nanjing University of Information Science and Technology Nanjing China Institute for AI in Medicine School of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing China School of Computing University of Leeds Leeds United Kingdom School of Medicine University of Leeds Leeds United Kingdom Department of Radiology and Nuclear Medicine Radboud University Medical Centre Nijmegen Netherlands Netherlands West China Hospital of Sichuan University Chengdu China Department of Cardiovascular Sciences KU Leuven Leuven Belgium Department of Electrical Engineering KU Leuven Leuven Belgium Alan Turing Institute London United Kingdom University of North Carolina Chapel Hill United States

Tumor infiltration of the recurrent laryngeal nerve (RLN) is a contraindication for robotic thyroidectomy and can be difficult to detect via standard laryngoscopy. Ultrasound (US) is a viable alternative for RLN detection due to its safety and ability to provide real-time feedback. However, the tininess of the RLN, with a diameter typically less than 3 mm, poses significant challenges to the accurate localization of the RLN. In this work, we propose a knowledge-driven framework for RLN localization, mimicking the standard approach surgeons take to identify the RLN according to its surrounding organs. We construct a prior anatomical model based on the inherent relative spatial relationships between organs. Through Bayesian shape alignment (BSA), we obtain the candidate coordinates of the center of a region of interest (ROI) that encloses the RLN. The ROI allows a decreased field of view for determining the refined centroid of the RLN using a dual-path identification network, based on multi-scale semantic information. Experimental results indicate that the proposed method achieves superior hit rates and substantially smaller distance errors compared with state-of-the-art methods. © 2022, CC BY.

关键词： Image segmentation

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Active legibility in multiagent reinforcement learning

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Artificial Intelligence 2025年 346卷

作者： Liu, Yanyu Pan, Yinghui Zeng, Yifeng Ma, Biyang Doshi, Prashant School of Automation Central South University No. 605 South Lushan Road Hunan Changsha410083 China School of Artificial Intelligence & National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Guangdong Shenzhen China Department of Computer and Information Sciences Northumbria University Newcastle United Kingdom School of Computer Science Minnan Normal University Fujian Zhangzhou China Department of Computer Science University of Georgia AthensGA United States

A multiagent sequential decision problem has been seen in many critical applications including urban transportation, autonomous driving cars, military operations, etc. Its widely known solution, namely multiagent reinforcement learning, has evolved tremendously in recent years. Among them, the solution paradigm of modeling other agents attracts our interest, which is different from traditional value decomposition or communication mechanisms. It enables agents to understand and anticipate others' behaviors and facilitates their collaboration. Inspired by recent research on the legibility that allows agents to reveal their intentions through their behavior, we propose a multiagent active legibility framework to improve their performance. The legibility-oriented framework drives agents to conduct legible actions so as to help others optimize their behaviors. In addition, we design a series of problem domains that emulate a common legibility-needed scenario and effectively characterize the legibility in multiagent reinforcement learning. The experimental results demonstrate that the new framework is more efficient and requires less training time compared to several multiagent reinforcement learning algorithms. © 2025 The Author(s)

关键词： Urban transportation

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Benchmarking the CoW with the TopCoW Challenge: Topology-Aware Anatomical Segmentation of the Circle of Willis for CTA and MRA

arXiv

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arXiv 2023年

作者： Yang, Kaiyuan Musio, Fabio Ma, Yihui Juchler, Norman Paetzold, Johannes C. Al-Maskari, Rami Höher, Luciano Li, Hongwei Bran Hamamci, Ibrahim Ethem Sekuboyina, Anjany Shit, Suprosanna Huang, Houjing Prabhakar, Chinmay de la Rosa, Ezequiel Waldmannstetter, Diana Kofler, Florian Navarro, Fernando Menten, Martin Ezhov, Ivan Rueckert, Daniel Vos, Iris Ruigrok, Ynte Velthuis, Birgitta Kuijf, Hugo Hämmerli, Julien Wurster, Catherine Bijlenga, Philippe Westphal, Laura Bisschop, Jeroen Colombo, Elisa Baazaoui, Hakim Makmur, Andrew Hallinan, James Wiestler, Bene Kirschke, Jan S. Wiest, Roland Montagnon, Emmanuel Letourneau-Guillon, Laurent Galdran, Adrian Galati, Francesco Falcetta, Daniele Zuluaga, Maria A. Lin, Chaolong Zhao, Haoran Zhang, Zehan Ra, Sinyoung Hwang, Jongyun Park, Hyunjin Chen, Junqiang Wodzinski, Marek Müller, Henning Shi, Pengcheng Liu, Wei Ma, Ting Yalçin, Cansu Hamadache, Rachika E. Salvi, Joaquim Llado, Xavier Estrada, Uma Maria Lal-Trehan Abramova, Valeriia Giancardo, Luca Oliver, Arnau Liu, Jialu Huang, Haibin Cui, Yue Lin, Zehang Liu, Yusheng Zhu, Shunzhi Patel, Tatsat R. Tutino, Vincent M. Orouskhani, Maysam Wang, Huayu Mossa-Basha, Mahmud Zhu, Chengcheng Rokuss, Maximilian R. Kirchhoff, Yannick Disch, Nico Holzschuh, Julius Isensee, Fabian Maier-Hein, Klaus Sato, Yuki Hirsch, Sven Wegener, Susanne Menze, Bjoern Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Center for Computational Health Zurich University of Applied Sciences Zurich Switzerland Department of Neuroradiology University Hospital of Zurich Zurich Switzerland Department of Neurosurgery Zhongnan Hospital of Wuhan University Wuhan China Department of Computing Imperial College London London United Kingdom Helmholtz Center Munich Germany Athinoula A. Martinos Center for Biomedical Imaging Harvard Medical School Boston United States School of Medicine Technical University of Munich Munich Germany Department of Informatics Technical University of Munich Munich Germany Helmholtz AI Helmholtz Munich Munich Germany Image Sciences Institute UMC Utrecht Utrecht Netherlands Department of Neurology UMC Utrecht Utrecht Netherlands Department of Radiology UMC Utrecht Utrecht Netherlands Department of Clinical Neurosciences Division of Neurosurgery Geneva University Hospitals Geneva Switzerland Department of Neurology University Hospital of Zurich Zurich Switzerland Department of Physiology University of Toronto Toronto Canada Department of Neurosurgery University Hospital of Zurich Zurich Switzerland Department of Diagnostic Imaging National University Hospital Singapore Department of Diagnostic and Interventional Neuroradiology University Hospital Berne University of Berne Berne Switzerland Montreal Canada Universitat Pompeu Fabra Barcelona Spain EURECOM Biot France Institute of Medical Technology Peking University Health Science Center Beijing China Hangzhou Genlight Medtech Co. Ltd. Hangzhou China Department of Artificial Intelligence Sungkyunkwan University Seoul Korea Republic of Department of Electrical and Computer Engineering Sungkyunkwan University Seoul Korea Republic of Shanghai MediWorks Precision Instruments Co. Ltd. Shanghai China Institute of Informatics HES-SO Valais-Wallis Switzerland Department of Measurement and Electronics AGH University

The Circle of Willis (CoW) is an important network of arteries connecting major circulations of the brain. Its vascular architecture is believed to affect the risk, severity, and clinical outcome of serious neuro-vascular diseases. However, characterizing the highly variable CoW anatomy is still a manual and time-consuming expert task. The CoW is usually imaged by two angiographic imaging modalities, magnetic resonance angiography (MRA) and computed tomography angiography (CTA), but there exist limited public datasets with annotations on CoW anatomy, especially for CTA. Therefore we organized the TopCoW Challenge in 2023 with the release of an annotated CoW dataset. The TopCoW dataset was the first public dataset with voxel-level annotations for thirteen possible CoW vessel components, enabled by virtual-reality (VR) technology. It was also the first large dataset with paired MRA and CTA from the same patients. TopCoW challenge formalized the CoW characterization problem as a multiclass anatomical segmentation task with an emphasis on topological metrics. We invited submissions worldwide for the CoW segmentation task, which attracted over 140 registered participants from four continents. The top performing teams managed to segment many CoW components to Dice scores around 90%, but with lower scores for communicating arteries and rare variants. There were also topological mistakes for predictions with high Dice scores. Additional topological analysis revealed further areas for improvement in detecting certain CoW components and matching CoW variant topology accurately. TopCoW represented a first attempt at benchmarking the CoW anatomical segmentation task for MRA and CTA, both morphologically and topologically. © 2023, CC BY-NC-ND.

关键词： Large datasets

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Supervised machine learning based routing detection for smart meter network 12

Supervised machine learning based routing detection for smar...

引用

12th EAI International Conference on Mobile Multimedia Communications, MOBIMEDIA 2019

作者： Hasan, Md Raqibull Zhao, Yanxiao Wang, Guodong Luo, Yu Pu, Lina Wang, Rui Department of Electrical and Computer Engineering South Dakota School of Mines and Technology United States Department of Electrical and Computer Engineering Virginia Commonwealth University United States Department of Computer Science Massachusetts College of Liberal Arts United States Department of Electrical and Computer Engineering Mississippi State University United States School of Computing Sciences and Computer Engineering University of Southern Mississippi United States College of Information Engineering and Automation Civil Aviation University of China China

ISBN: (纸本)9781631901836

It is known that the Ad hoc On-Demand Distance Vector (AODV) routing protocol for smart meter network is vulnerable to denial of service attacks (e.g., black hole attack and selective forwarding attack). In this paper, we introduce supervised machine learning to detect unknown routing attacks under AODV. There are two problems in the existing intrusion detection algorithms. The first problem is that the existing intrusion detection algorithms are mainly applied to a specific and known type of routing attack, which no longer work for unknown attacks. The second one is that constant thresholds are commonly used for detection. To overcome these two problems, we introduce a supervised machine learning based detection approach. To implement supervised machine learning, three steps are involved. First, features and target estimations are selected from malicious AODV behaviors in smart meter network to generate training data sets. Second, we assign a suitable classifier including support vector machine, k-nearest neighbors and decision trees to fit the training and predicted data. Third, we update our training data to maintain a dynamic threshold. Simulations are conducted using Python3.6 to evaluate the accuracy and the time overhead of our proposed supervised machine learning model. The simulation results show that the decision trees algorithm assures 100% accuracy with minimum time overhead to detect routing attacks in AODV. © 2019 EAI.

关键词： Decision trees

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Software for Small-scale Robotics: A Review

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International Journal of automation and computing 2018年第5期15卷 515-524页

作者： Tobias Tiemerding Sergej Fatikow Department of Computing Science Division Microrobotics and Control Engineering (AMiR) University of Oldenburg OFFIS–Institute for Information Technology R&D Division Automation and Integration Technology (AIT)

In recent years, a large number of relatively advanced and often ready-to-use robotic hardware components and systems have been developed for small-scale use. As these tools are mature, there is now a shift towards advanced applications. These often require automation and demand reliability, efficiency and decisional autonomy. New software tools and algorithms for artificial intelligence（AI） and machine learning（ML） can help here. However, since there are many software-based control approaches for small-scale robotics, it is rather unclear how these can be integrated and which approach may be used as a starting point. Therefore, this paper attempts to shed light on existing approaches with their advantages and disadvantages compared to established requirements. For this purpose, a survey was conducted in the target group. The software categories presented include vendor-provided software, robotic software frameworks（RSF）, scientific software and in-house developed software（IHDS）. Typical representatives for each category are described in detail, including Smar Act precision tool commander, Math Works Matlab and national instruments Lab VIEW, as well as the robot operating system（ROS）. The identified software categories and their representatives are rated for end user satisfaction based on functional and non-functional requirements, recommendations and learning curves. The paper concludes with a recommendation of ROS as a basis for future work.

关键词： Robotic control software engineering micro/nano robotics artificial intelligence （AI） machine learning （ML） open

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COVID-19 detection in CT images with deep learning: A voting-based scheme and cross-datasets analysis

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Informatics in Medicine Unlocked 2020年 20卷 100427页

作者： Silva, Pedro Luz, Eduardo Silva, Guilherme Moreira, Gladston Silva, Rodrigo Lucio, Diego Menotti, David Computing Department Universidade Federal de Ouro Preto (UFOP) MG Brazil Department of Control and Automation Engineering Universidade Federal de Ouro Preto (UFOP) MG Brazil Department of Informatics Universidade Federal do Parana (UFPR) PR Brazil

Early detection and diagnosis are critical factors to control the COVID-19 spreading. A number of deep learning-based methodologies have been recently proposed for COVID-19 screening in CT scans as a tool to automate and help with the diagnosis. These approaches, however, suffer from at least one of the following problems: (i) they treat each CT scan slice independently and (ii) the methods are trained and tested with sets of images from the same dataset. Treating the slices independently means that the same patient may appear in the training and test sets at the same time which may produce misleading results. It also raises the question of whether the scans from the same patient should be evaluated as a group or not. Moreover, using a single dataset raises concerns about the generalization of the methods. Different datasets tend to present images of varying quality which may come from different types of CT machines reflecting the conditions of the countries and cities from where they come from. In order to address these two problems, in this work, we propose an Efficient Deep Learning Technique for the screening of COVID-19 with a voting-based approach. In this approach, the images from a given patient are classified as group in a voting system. The approach is tested in the two biggest datasets of COVID-19 CT analysis with a patient-based split. A cross dataset study is also presented to assess the robustness of the models in a more realistic scenario in which data comes from different distributions. The cross-dataset analysis has shown that the generalization power of deep learning models is far from acceptable for the task since accuracy drops from 87.68% to 56.16% on the best evaluation scenario. These results highlighted that the methods that aim at COVID-19 detection in CT-images have to improve significantly to be considered as a clinical option and larger and more diverse datasets are needed to evaluate the methods in a realistic scenario. © 2020 The Author

关键词： Chest radiography COVID-19 Deep learning EfficientNet Pneumonia

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