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Corrigendum to “An Optimized RBF Neural Network Based on Beetle Antennae Search Algorithm for Modeling the Static Friction in a Robotic Manipulator Joint”

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Mathematical Problems in engineering 2020年第1期2020卷

作者： Yuxiang Wang Zhangwei Chen Hongfei Zu Xiang Zhang State Key Laboratory of Fluid Power and Mechatronics Systems Zhejiang University Hangzhou 310000 *** Faculty of Mechanical Engineering and Automation Zhejiang Sci-Tech University Hangzhou 310000 *** Institute of Computer Science Hangzhou Dianzi University Hangzhou 310000 ***

Nonlinear analysis of dynamical complex networks 2014

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Abstract and Applied Analysis

Abstract and Applied Analysis 2014年第1期2014卷

作者： Wang, Zidong Shen, Bo Dong, Hongli Hu, Jun He, Xiao Ding, Derui Department of Computer Science Brunel University Uxbridge Middlesex UB8 3PH United Kingdom Communication Systems and Networks (CSN) Research Group Faculty of Engineering King Abdulaziz University Jeddah 21589 Saudi Arabia School of Information Science and Technology Donghua University Shanghai 200051 China College of Electrical and Information Engineering Northeast Petroleum University Daqing 163318 China Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin 150001 China Department of Applied Mathematics Harbin University of Science and Technology Harbin 150080 China Department of Automation Tsinghua University Beijing 100084 China Department of Applied Mathematics Anhui Polytechnic University Wuhu 241000 China

Erratum to “Passivity-based control technique for integration of DG resources into the power grid” [Int. J. Electr. Power Energy Syst. 58 (2014) 281–290]

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International Journal of Electrical Power & Energy Systems 2015年 65卷 469-469页

作者： Majid Mehrasa M. Ebrahim Adabi Edris Pouresmaeil Jafar Adabi Young Researchers and Elite Club Sari Branch Islamic Azad University Sari Iran Faculty of Electrical Engineering Iran University of Science and Technology Center of Excellence for Power System Automation and Operation Tehran Iran Centre for Smart Energy Solutions Faculty of Engineering University of Southern Denmark (SDU) Odense Denmark Faculty of Electrical and Computer Engineering Babol (Noshirvani) University of Technology Babol Iran

INSTANCE SEGMENTATION AND TEETH CLASSIFICATION IN PANORAMIC X-RAYS

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arXiv 2024年

作者： Budagam, Devichand Ghosh, Sayan Imanbayev, Azamat Zhanatuly Kaplun, Dmitrii Rychenkov, Artem Sinitca, Aleksandr Kumar, Ayush Shrivastav, Anuj Akhmetov, Iskander Rafailovich Antonov, Sergey Cyganov, Gleb Department of Computer Science and Engineering Indian Institute of Technology Kharagpur Kharagpur India Department of Chemical Engineering Indian Institute of Technology Kharagpur Kharagpur India School of IT and Engineering Kazakh-British Technical University Almaty Kazakhstan Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou China Department of Automation and Control Processes St. Petersburg Electrotechnical University"LETI" St. Petersburg Russia Centre for Digital Telecommunication Technologies St. Petersburg Electrotechnical University"LETI" St. Petersburg Russia Department of Humanities and Social Sciences Indian Institute of Technology Kharagpur Kharagpur India Department of Architecture and Regional Planning Indian Institute of Technology Kharagpur Kharagpur India Faculty of Digital Transformation ITMO University St. Petersburg Russia

Teeth segmentation and recognition are critical in various dental applications and dental diagnosis. Automatic and accurate segmentation approaches have been made possible by integrating deep learning models. Although teeth segmentation has been studied in the past, only some techniques were able to effectively classify and segment teeth simultaneously. This article offers a pipeline of two deep learning models, U-Net and YOLOv8, which results in BB-UNet, a new architecture for the classification and segmentation of teeth on panoramic X-rays that is efficient and reliable. We have improved the quality and reliability of teeth segmentation by utilising the YOLOv8 and U-Net capabilities. The proposed networks have been evaluated using the mean average precision (mAP) and dice coefficient for YOLOv8 and BB-UNet, respectively. We have achieved a 3% increase in mAP score for teeth classification compared to existing methods, and a 10-15% increase in dice coefficient for teeth segmentation compared to U-Net across different categories of teeth. A new Dental dataset was created based on UFBA-UESC dataset with Bounding-Box and Polygon annotations of 425 dental panoramic X-rays. The findings of this research pave the way for a wider adoption of object detection models in the field of dental diagnosis. © 2024, CC BY.

关键词： Object detection

CEBRA Method: Decoding Brain Activity for Advanced Brain-computer Interface Technology

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CEBRA Method: Decoding Brain Activity for Advanced Brain-Com...

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

作者： Jingcheng Yang Frank Kulwa Xuanwei Liu Yiqing Lu Yufa Fu Guanglin Li Yaping Huai Xin Zhang Yongcheng Li CAS Key Laboratory of Human-Machine Intelligence Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences (CAS) Shenzhen China Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen China Shenzhen Dapeng New District Nanao People’s Hospital Shenzhen China Department of Rehabilitation Medicine Shenzhen Longhua District Central Hospital Shenzhen China Brain Cognition and Brain-Computer Intelligence Integration Group Kunming University of Science and Technology Kunming China Shandong Zhongke Advanced Technology Co. Ltd. Jinan China

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

The emerging neurorehabilitation technology, Brain-computer Interface (BCI), provides a novel prospect for stroke recovery. However, decoding brain activity during the movement present substantial challenges, and feature extraction is crutial to build a better decoder. In this study the CEBRA method were employed to extract features first based on electroencephalogram(EEG) data during Motor Execution(ME) and Motor lmagery (Ml) tasks for 20 participants (including 10 stroke patients). The results revealed that, in MI tasks, CEBRA-RF (Random Forests) achieved an average classification accuracy of 91.33%, with an average F1-score of 91.19%, and CEBRA-SVM (Support Vector Machine) achieved an average classification accuracy of 91.32%, with an average F1-score of 90.83%. Compared to other conventional feature extraction methods, CEBRA shows significantly higher accuracy (t-test, p<0.01). However, in ME tasks, CEBRA-RF achieved an average classification accuracy of 75.67%, with an average F1-score of 75.39%, and CEBRA-SVM achieved an average classification accuracy of 76.13%, with an average F1-score of 75.80%. Nevertheless, no significant differences were observed compared to other feature extraction methods. These findings demonstrate the potential of CEBRA in decoding patients’ brain activity. The results of this study hold promise in addressing the current challenges of low decoding accuracy in BCI systems, offering a new approach for designing BCI-assisted rehabilitation systems for stroke patients.

关键词： Support vector machines Brain Accuracy Stroke (medical condition) Feature extraction Motors Brain-computer interfaces Neurorehabilitation Decoding Paralysis

ChaLearn looking at people: IsoGD and ConGD large-scale RGB-D gesture recognition

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arXiv 2019年

作者： Wan, Jun Lin, Chi Wen, Longyin Li, Yunan Miao, Qiguang Escalera, Sergio Anbarjafari, Gholamreza Guyon, Isabelle Guo, Guodong Li, Stan Z. National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China JD Finance Mountain ViewCA United States University of Southern California Los AngelesCA90089-0911 United States School of Computer Science and Technology Xidian University & Xi'an Key Laboratory of Big Data and Intelligent Vision 2nd South Taibai Road Xi'an710071 China Universitat de Barcelona Computer Vision Center Spain iCV Lab Institute of Technology University of Tartu Estonia Faculty of Engineering Hasan Kalyoncu University Gaziantep Turkey Institute of Digital Technologies Loughborough University London United Kingdom ChaLearn United States University Paris-Saclay France institute of Deep Learning Baidu Research National Engineering Laboratory for Deep Learning Technology and Application China

The ChaLearn large-scale gesture recognition challenge has been run twice in two workshops in conjunction with the International Conference on Pattern Recognition (ICPR) 2016 and International Conference on computer Vision (ICCV) 2017, attracting more than 200 teams around the world. This challenge has two tracks, focusing on isolated and continuous gesture recognition, respectively. This paper describes the creation of both benchmark datasets and analyzes the advances in large-scale gesture recognition based on these two datasets. We discuss the challenges of collecting large-scale ground-truth annotations of gesture recognition, and provide a detailed analysis of the current state-of-the-art methods for large-scale isolated and continuous gesture recognition based on RGB-D video sequences. In addition to recognition rate and mean jaccard index (MJI) as evaluation metrics used in our previous challenges, we also introduce the corrected segmentation rate (CSR) metric to evaluate the performance of temporal segmentation for continuous gesture recognition. Furthermore, we propose a bidirectional long short-term memory (Bi-LSTM) baseline method, determining the video division points based on the skeleton points extracted by convolutional pose machine (CPM). Experiments demonstrate that the proposed Bi-LSTM outperforms the state-of-the-art methods with an absolute improvement of 8.1% (from 0.8917 to 0.9639) of CSR. Copyright © 2019, The Authors. All rights reserved.

关键词： Long short-term memory

Correction to: A hybrid machine learning framework to predict mortality in paralytic ileus patients using electronic health records (EHRs)

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Journal of Ambient Intelligence and Humanized Computing 2020年第10期14卷 14367-14367页

作者： Ahmed, Fahad Shabbir Ali, Liaqat Raza Ul Mustafa Khattak, Hasan Ali Hameed, Tahir Wajahat, Iram Kadry, Seifedine Bukhari, Syed Ahmad Chan Department of Pathology Yale School of Medicine Yale University New Haven USA School of Information and Communication Engineering University of Electronic Science and Technology of China (UESTC) Chengdu China Department of Electrical Engineering University of Science and Technology Bannu Pakistan Department of Computer Engineering and Industrial Automation (DCA) School of Electrical and Computer Engineering (FEEC) Unicamp Brazil Department of Computer Science COMSATS University Islamabad Islamabad Pakistan Girard School of Business Merrimack College North Andover USA Allied Institute of Medical Sciences Ahmad College of Pharmacy Gujrat Pakistan Department of Mathematics and Computer Science Faculty of Science Beirut Arab University Beirut Lebanon Division of Computer Science Mathematics and Science Collins College of Professional Studies St. John’s University New York USA

In the original publication of the article, the author names Fahad Shabbir Ahmad and Raza-Ul-Mustafa was incorrectly published.

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Robust medical instrument segmentation challenge 2019

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arXiv 2020年

作者： Roβ, Tobias Reinke, Annika Full, Peter M. Wagner, Martin Kenngott, Hannes Apitz, Martin Hempe, Hellena Filimon, Diana Mindroc Scholz, Patrick Tran, Thuy Nuong Bruno, Pierangela Arbelaéz, Pablo Bian, Gui-Bin Bodenstedt, Sebastian Bolmgren, Jon Lindström Bravo-Sánchez, Laura Chen, Hua-Bin González, Cristina Guo, Dong Halvorsen, Pål Heng, Pheng-Ann Hosgor, Enes Hou, Zeng-Guang Isensee, Fabian Jha, Debesh Jiang, Tingting Kirtac, Kadir Jin, Yueming Kletz, Sabrina Leger, Stefan Li, Zhixuan Maier-Hein, Klaus H. Ni, Zhen-Liang Riegler, Michael A. Schoeffmann, Klaus Shi, Ruohua Speidel, Stefanie Stenze, Michael Twick, Isabell Wang, Gutai Wang, Jiacheng Wang, Liansheng Wang, Lu Zhang, Yujie Zhou, Yan-Jie Zhu, Lei Wiesenfarth, Manuel Kopp-Schneider, Annette Müller-Stich, Beat P. Maier-Hein, Lena Heidelberg Germany University of Heidelberg Germany Heidelberg Germany Department of General Heidelberg University Hospital Visceral and Transplant Surgery Heidelberg Germany caresyntax Berlin Germany University of Chinese Academy Sciences Beijing China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China SimulaMet Oslo Norway Tromsø Norway Oslo Norway School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu China Partner Site Dresden Heidelberg Germany Faculty of Medicine and University Hospital Carl Gustav Carus Technische Universität Dresden Dresden Germany Dresden Germany Universidad de Los Andes Bogotá Colombia Peking University Peking China Department of Computer Science School of Informatics Xiamen University Xiamen China Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong German Cancer Research Center Division of Biostatistics Heidelberg Germany Institute of Information Technology Klagenfurt University Austria

Intraoperative tracking of laparoscopic instruments is often a prerequisite for computer and roboticassisted interventions. While numerous methods for detecting, segmenting and tracking of medical instruments based on endoscopic video images have been proposed in the literature, key limitations remain to be addressed: Firstly, robustness, that is, the reliable performance of state-of-the-art methods when run on challenging images (e.g. in the presence of blood, smoke or motion artifacts). Secondly, generalization;algorithms trained for a specific intervention in a specific hospital should generalize to other interventions or institutions. In an effort to promote solutions for these limitations, we organized the Robust Medical Instrument Segmentation (ROBUST-MIS) challenge as an international benchmarking competition with a specific focus on the robustness and generalization capabilities of algorithms. For the first time in the field of endoscopic image processing, our challenge included a task on binary segmentation and also addressed multi-instance detection and segmentation. The challenge was based on a surgical data set comprising 10,040 annotated images acquired from a total of 30 surgical procedures from three different types of surgery. The validation of the competing methods for the three tasks (binary segmentation, multi-instance detection and multi-instance segmentation) was performed in three different stages with an increasing domain gap between the training and the test data. The results confirm the initial hypothesis, namely that algorithm performance degrades with an increasing domain gap. While the average detection and segmentation quality of the best-performing algorithms is high, future research should concentrate on detection and segmentation of small, crossing, moving and transparent instrument(s) (parts). Copyright © 2020, The Authors. All rights reserved.

关键词： Endoscopy

MACHINE LEARNING APPROACHES FOR COVID-19 DETECTION FROM CHEST X-RAY IMAGING: A SYSTEMATIC REVIEW

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arXiv 2022年

作者： Arteaga-Arteaga, Harold Brayan delaPava, Melissa Mora-Rubio, Alejandro Bravo-Ortíz, Mario Alejandro Alzate-Grisales, Jesus Alejandro Arias-Garzón, Daniel López-Murillo, Luis Humberto Buitrago-Carmona, Felipe Villa-Pulgarín, Juan Pablo Mercado-Ruiz, Esteban Orozco-Arias, Simon Hassaballah, M. de la Iglesia-Vaya, Maria Cardona-Morales, Oscar Tabares-Soto, Reinel Department of Electronics and Automation Universidad Autónoma de Manizales Manizales Colombia Department of Chemical Engineering Universidad Nacional de Colombia Manizales Colombia Department of Computer Science Universidad Autónoma de Manizales Manizales Colombia Department of Systems and Informatics Universidad de Caldas Manizales Colombia Faculty of Computers and Information South Valley University Qena Egypt Unidad Mixta de Imagen Biomédica FISABIO-CIPF Fundación para el Fomento de la Investigación Sanitario y Biomédica de la Comunidad Valenciana Valencia Spain

There is a necessity to develop affordable, and reliable diagnostic tools, which allow containing the COVID-19 spreading. Machine Learning (ML) algorithms have been proposed to design support decision-making systems to assess chest X-ray images, which have proven to be useful to detect and evaluate disease progression. Many research articles are published around this subject, which makes it difficult to identify the best approaches for future work. This paper presents a systematic review of ML applied to COVID-19 detection using chest X-ray images, aiming to offer a baseline for researchers in terms of methods, architectures, databases, and current limitations. © 2022, CC BY.

关键词： COVID-19