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SSRN 2022年

作者： Liu, Zhenzhong Zhou, Yifan Sun, Qiyuan Wang, Yan Polat, Kemal Nour, Majid Alenezi, Fayadh Alhudhaif, Adi Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin300384 China Tianjin300384 China Department of Electrical and Electronics Engineering Bolu Abant Izzet Baysal University Bolu14280 Turkey Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia Department of Electrical Engineering College of Engineering Jouf University 72238 Saudi Arabia Department of Computer Science College of Computer Engineering and Sciences in Al-kharj Prince Sattam bin Abdulaziz University P.O. Box 151 Al-Kharj 11942 Saudi Arabia

Surgical instrument detection and tracking is one of the core technologies of surgical robots, which can assist clinicians in completing operations. However, existing methods need further improvement in terms of real-time performance and accuracy. We propose a deep learning algorithm, SI-YOLO, for detection of surgical instruments. SimAM attention mechanism is used to improve the feature extraction ability of YOLOv5. Replacing PANet with BiFPN in the neck network improves the feature fusion strategy. The α-IoU loss function is used to improve the regression accuracy of detection boxes. The mAP and FPS of the SI-YOLO reach 99.2% and 120 frames/sec, respectively. Aiming at the problem of ID transformation in the disappearance and occlusion of surgical instruments, the Inception-ResNet-v2 structure is added on the basis of the appearance feature extraction network Cosine of the DeepSort tracking algorithm. Combining SI-YOLO with the improved DeepSort implements the Tracking by Detection (TBD) algorithm. In the case of device disappearance or occlusion, the frequency of ID transitions is reduced by approximately 30%. Finally, we built a simulated surgery experimental platform, established our own data set, and realized the preliminary application of the proposed algorithm. © 2022, The Authors. All rights reserved.

关键词： Object detection

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Stochastic Model Predictive control with a Safety Guarantee for Automated Driving: Extended Version

arXiv

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arXiv 2020年

作者： Brüdigam, Tim Olbrich, Michael Wollherr, Dirk Leibold, Marion The Automatic Control Engineering The Technical University of Munich Munich Germany The Department of Computer Science The University of Augsburg Augsburg Germany

Automated vehicles require efficient and safe planning to maneuver in uncertain environments. Largely this uncertainty is caused by other traffic participants, e.g., surrounding vehicles. Future motion of surrounding vehicles is often difficult to predict. Whereas robust control approaches achieve safe, yet conservative motion planning for automated vehicles, Stochastic Model Predictive control (SMPC) provides efficient planning in the presence of uncertainty. Probabilistic constraints are applied to ensure that the maximal risk remains below a predefined level. However, safety cannot be ensured as probabilistic constraints may be violated, which is not acceptable for automated vehicles. Here, we propose an efficient trajectory planning framework with safety guarantees for automated vehicles. SMPC is applied to obtain efficient vehicle trajectories for a finite horizon. Based on the first optimized SMPC input, a guaranteed safe backup trajectory is planned using reachable sets. This backup is used to overwrite the SMPC input if necessary for safety. Recursive feasibility of the safe SMPC algorithm is proved. Highway simulations show the effectiveness of the proposed method regarding performance and safety. Copyright © 2020, The Authors. All rights reserved.

关键词： Model predictive control

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Evaluation of the ARCore Indoor Localization Technology

Evaluation of the ARCore Indoor Localization Technology

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Roedunet International Conference (RoEduNet)

作者： Anca Morar Maria Anca Băluţoiu Alin Moldoveanu Florica Moldoveanu Alex Butean Victor Asavei University POLITEHNICA of Bucharest Faculty of Automatic Control and Computer Science Bucharest Romania Lucian Blaga University of Sibiu Faculty of Engineering Industrial Software Sibiu Romania

ISBN: (数字)9780738112657

ISBN: (纸本)9781665418850

Augmented reality has become a very powerful tool nowadays. With the recent technological advance, smartphones have the ability to display augmented content on the screen. What makes the difference is the naturalness with which the multimedia content is superimposed over the video stream acquired with the phone's camera. This perfect overlay depends on the accurate estimation of the position and orientation of the smartphone's camera relative to the 3D representation of the space. ARCore is an augmented reality framework that computes the position and orientation of the smartphone. In order to assess the possibility of integrating ARCore within a virtual and augmented reality platform, we evaluated the accuracy of the ARCore localization technology, using the HTC Vive system as a reference. We designed an evaluation methodology, along with visualization tools that enable a thorough comparison between these two technologies.

关键词： Location awareness Smart phones Augmented reality Cameras Trajectory Three-dimensional displays computer science

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Research on Main Steam Temperature Prediction Model Based on Improved LSTM Algorithm

Research on Main Steam Temperature Prediction Model Based on...

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作者： Ling Zheng Xin Ye Fei Chen Department of Computer Science and Technology North China Electric Power University Faculty of Control and Computer Engineering

In order to improve the reliability and accuracy of the main steam temperature trend prediction, a main steam temperature prediction model based on improved LSTM is proposed. Firstly, uses the grey correlation analysis method to select the important influencing factors. Then, a linear structure is introduced into the LSTM structure to construct a main steam temperature prediction model. Finally, based on the historical operating data of the thermal power unit, a simulation experiment is performed to compare the prediction error of the output of the RNN model, the LSTM model, and the improved LSTM model. The results show that the method has higher prediction accuracy for the main steam temperature. At the same time, compared with other traditional methods, this method has better fitting effect, which can be well applied in practical engineering.

关键词： Main steam temperature grey correlation analysis deep learning improved LSTM algorithm regression prediction

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Risk Bounded Nonlinear Robot Motion Planning With Integrated Perception & control

arXiv

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arXiv 2022年

作者： Renganathan, Venkatraman Safaoui, Sleiman Kothari, Aadi Gravell, Benjamin Shames, Iman Summers, Tyler Department of Automatic Control Lund University Naturvetarvägen 18 SE Lund221 00 Sweden Eric Jonsson School of Engineering & Computer Science The University of Texas at Dallas 800 W Campbell Rd RichardsonTX75080 United States School of Engineering Massachusetts Institute of Technology CambridgeMA02139 United States CIICADA Lab School of Engineering Australian National University ActonACT0200 Australia

Robust autonomy stacks require tight integration of perception, motion planning, and control layers, but these layers often inadequately incorporate inherent perception and prediction uncertainties, either ignoring them altogether or making questionable assumptions of Gaussianity. Robots with nonlinear dynamics and complex sensing modalities operating in an uncertain environment demand more careful consideration of how uncertainties propagate across stack layers. We propose a framework to integrate perception, motion planning, and control by explicitly incorporating perception and prediction uncertainties into planning so that risks of constraint violation can be mitigated. Specifically, we use a nonlinear model predictive control based steering law coupled with a decorrelation scheme based Unscented Kalman Filter for state and environment estimation to propagate the robot state and environment uncertainties. Subsequently, we use distributionally robust risk Copyright © 2022, The Authors. All rights reserved.

关键词： Motion planning

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Extraction of image features based on the HOG method in the HSV color space

Extraction of image features based on the HOG method in the ...

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International Conference on sciences and Techniques of automatic control and computer engineering (STA)

作者： Fayçal Hamdaoui Sana Bougharriou Manel Gueddari Anis Sakly Electrical Department Laboratory of Control Electrical Systems and Environment (LAESE) National Engineering School of Monastir University of Monastir Monastir Tunisia Laboratory of EμE Faculty of Sciences of Monastir University of Monastir Monastir Tunisia Higher Institute of Computer Science and Mathematics University of Monastir Monastir Tunisia

ISBN: (纸本)9781665482622

In this paper, a fast, effective and robust features extraction system has been developed. To successfully design this hardware architecture, a conversion from RGB to HSV was applied. Likewise, hardware architecture of Histogram of Oriented Gradient (HOG) method was proposed. Vivado System Generator (VSG) tool was used to design architectures. To validate the performance of the proposed system, Benchmarks was applied. Moreover, execution time and resources utilization demonstrated a promising quality of such proposed system.

关键词： Histograms automatic frequency control Time-frequency analysis Image color analysis computer architecture Feature extraction Hardware

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Trend and Priority Change of Climate Change Mitigation Technology in the Global Mining Sector

SSRN

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SSRN 2021年

作者： Yamshitaa, Andre Shigueo Fujii, Hidemichi Luleå University of Technology Control Engineering Group Department of Computer Science Electrical and Space Engineering Luleå Sweden Faculty of Economics Kyushu University 744 Motooka Nishi-ku Fukuoka819-0395 Japan

The purpose of this work is to explore the most relevant driving factors for innovation in the mining, mineral processing and metals sectors related to climate change mitigation technology. In order to do so, we use a decomposition analysis based on the logarithmic mean Divisia index to analyze the trends in patent families related to climate change mitigation technology filled by organizations or individuals in the mining, mineral processing and metals sectors. collected from the Orbis IP patent database between 2001 and 2016. We observed that three different factors, namely the PRIORITY of climate change mitigation technology;the overall relevance of the MINING, mineral processing and metals sector;and the SCALE of patent families have a different impact in different countries and country groups, depending on whether the mining and minerals sector play a strong role in their economy or whether they are a developed or developing country. Two shortcomings of this work are: we were not able to address the fact that some authors question the use of patent applications as a proxy for investments in R&D, and we did not analyze the shifts in innovation patterns from companies. Nonetheless, our findings encourage policy recommendations reported by other authors, especially with regards to the economic development of poorer countries, technology transfer and coordination between innovation stakeholders. © 2021, The Authors. All rights reserved.

关键词： Patents and inventions

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A spectral small-gain condition for input-to-state stability of infinite networks

引用

IFAC-PapersOnLine 2020年第2期53卷 5303-5308页

作者： Christoph Kawan Andrii Mironchenko Abdalla Swikir Navid Noroozi Majid Zamani Institute of Informatics Ludwig Maximilian University Munich Germany Faculty of Computer Science and Mathematics University of Passau Germany Department of Electrical and Computer Engineering Technical University of Munich Germany Otto-von-Guericke University Magdeburg Laboratory for Systems Theory and Automatic Control Germany Computer Science Department University of Colorado Boulder USA

This paper presents a tight small-gain theorem for networks composed of infinitely many finite-dimensional subsystems. Assuming that each subsystem is exponentially input-to-state stable, we show that if the gain operator, collecting all the information about the internal Lyapunov gains, has a spectral radius less than one, the overall infinite network is exponentially input-to-state stable. We illustrate the effectiveness of our result by applying it to traffic networks.

关键词： Nonlinear systems small-gain theorems infinite-dimensional systems input-to-state stability Lyapunov methods large-scale systems

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Efficient MedSAMs: Segment Anything in Medical Images on Laptop

arXiv

引用

arXiv 2024年

作者： Ma, Jun Li, Feifei Kim, Sumin Asakereh, Reza Le, Bao-Hiep Nguyen-Vu, Dang-Khoa Pfefferle, Alexander Wei, Muxin Gao, Ruochen Lyu, Donghang Yang, Songxiao Purucker, Lennart Marinov, Zdravko Staring, Marius Lu, Haisheng Dao, Thuy Thanh Ye, Xincheng Li, Zhi Brugnara, Gianluca Vollmuth, Philipp Foltyn-Dumitru, Martha Cho, Jaeyoung Mahmutoglu, Mustafa Ahmed Bendszus, Martin Pflüger, Irada Rastogi, Aditya Ni, Dong Yang, Xin Zhou, Guang-Quan Wang, Kaini Heller, Nicholas Papanikolopoulos, Nikolaos Weight, Christopher Tong, Yubing Udupa, Jayaram K. Patrick, Cahill J. Wang, Yaqi Zhang, Yifan Contijoch, Francisco McVeigh, Elliot Ye, Xin He, Shucheng Haase, Robert Pinetz, Thomas Radbruch, Alexander Krause, Inga Kobler, Erich He, Jian Tang, Yucheng Yang, Haichun Huo, Yuankai Luo, Gongning Kushibar, Kaisar Amankulov, Jandos Toleshbayev, Dias Mukhamejan, Amangeldi Egger, Jan Pepe, Antonio Gsaxner, Christina Luijten, Gijs Fujita, Shohei Kikuchi, Tomohiro Wiestler, Benedikt Kirschke, Jan S. de la Rosa, Ezequiel Bolelli, Federico Lumetti, Luca Grana, Costantino Xie, Kunpeng Wu, Guomin Puladi, Behrus Martín-Isla, Carlos Lekadir, Karim Campello, Victor M. Shao, Wei Brisbane, Wayne Jiang, Hongxu Wei, Hao Yuan, Wu Li, Shuangle Zhou, Yuyin Wang, Bo AI Collaborative Centre University Health Network Department of Laboratory Medicine and Pathobiology University of Toronto Vector Institute Toronto Canada Peter Munk Cardiac Centre University Health Network Toronto Canada Toronto General Hospital Research Institute University Health Network Department of Computer Science University of Toronto University Health Network Vector Institute Toronto Canada University of Science Vietnam National University Ho Chi Minh City Viet Nam Institute of Computer Science University of Freiburg Freiburg Germany School of Medicine and Health Harbin Institute of Technology Harbin China Division of Image Processing Department of Radiology Leiden University Medical Center Leiden Netherlands Department of System and Control Engineering School of Engineering Institute of Science Tokyo Formerly Tokyo Institute of Technology Tokyo Japan Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China School of Electrical Engineering and Computer Science University of Queensland Brisbane Australia School of Cyberspace Hangzhou Dianzi University Hangzhou China Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany Division for Computational Radiology and Clinical AI Department of Neuroradiology University Hospital Bonn Germany School of Biomedical Engineering Shenzhen University Shenzhen China School of Biological Science and Medical Engineering Southeast University Nanjing China Department of Urology Cleveland Clinic Cleveland United States Department of Computer Science University of Minnesota Minneapolis United St

Promptable segmentation foundation models have emerged as a transformative approach to addressing the diverse needs in medical images, but most existing models require expensive computing, posing a big barrier to their adoption in clinical practice. In this work, we organized the first international competition dedicated to promptable medical image segmentation, featuring a large-scale dataset spanning nine common imaging modalities from over 20 different institutions. The top teams developed lightweight segmentation foundation models and implemented an efficient inference pipeline that substantially reduced computational requirements while maintaining state-of-the-art segmentation accuracy. Moreover, the post-challenge phase advanced the algorithms through the design of performance booster and reproducibility tasks, resulting in improved algorithms and validated reproducibility of the winning solution. Furthermore, the best-performing algorithms have been incorporated into the open-source software with a user-friendly interface to facilitate clinical adoption. The data and code are publicly available to foster the further development of medical image segmentation foundation models and pave the way for impactful real-world applications. © 2024, CC BY-NC-SA.

关键词： Open source software

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Label Cluster Chains for Multi-Label Classification

arXiv

引用

arXiv 2024年

作者： Gatto, Elaine Cecília Nakano, Felipe Kenji Read, Jesse Ferrandin, Mauri Cerri, Ricardo Vens, Celine Department of Computer Science Federal University of São Carlos SP São Carlos13565-905 Brazil Faculty of Medicine Katholieke Universiteit Leuven West Flanders Kortrijk8500 Belgium École Polytechnique Polytechnic Institute of Paris Palaiseau Paris91120 France Department of Control Automation and Computing Engineering Federal University of Santa Catarina SC Blumenau89036-002 Brazil Institute of Mathematical and Computing Sciences University of São Paulo SP São Carlos13566-590 Brazil

Multi-label classification is a type of supervised machine learning that can simultaneously assign multiple labels to an instance. To solve this task, some methods divide the original problem into several sub-problems (local approach), others learn all labels at once (global approach), and others combine several classifiers (ensemble approach). Regardless of the approach used, exploring and learning label correlations is important to improve the classifier predictions. Ensemble of Classifier Chains (ECC) is a well-known multi-label method that considers label correlations and can achieve good overall performance on several multi-label datasets and evaluation measures. However, one of the challenges when working with ECC is the high dimensionality of the label space, which can impose limitations for fully-cascaded chains as the complexity increases regarding feature space expansion. To improve classifier chains, we propose a method to chain disjoint correlated label clusters obtained by applying a partition method in the label space. During the training phase, the ground truth labels of each cluster are used as new features for all of the following clusters. During the test phase, the predicted labels of clusters are used as new features for all the following clusters. Our proposal, called Label Cluster Chains for Multi-Label Classification (LCC-ML), uses multi-label Random Forests as base classifiers in each cluster, combining their predictions to obtain a final multi-label classification. Our proposal obtained better results compared to the original ECC. This shows that learning and chaining disjoint correlated label clusters can better explore and learn label correlations. © 2024, CC BY.

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