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IEEE International Conference on Civil Aviation Safety and Information technology (ICCASIT)

作者： Keqin Li Jiajing Chen Dezhi Yu Tao Dajun Xinyu Qiu Jieting Lian Ryan Ji Shengyuan Zhang Zhenyu Wan Baiwei Sun Bo Hong Fanghao Ni Jiaqi Han Department of Computer Science AMA University Quezon Philippines Courant Institute New York University 251 Mercer St New York NY USA School of Information University of California Berkeley University Avenue and Oxford St Berkeley CA United States Master of Science in Mechanical Engineering School of Engineering Carnegie Mellon University 5000 Forbes Avenue Pittsburgh PA USA College of Engineering Northeastern University Seattle USA Master of Science in Database Technology School of Professional Studies New York University 50 West 4th Street New York NY USA College of Engineering Cornell University 616 Thurston Ave Ithaca NY US Master of Professional Science in Applied Statistics-Data Science College of Computing and Information Science Cornell University 616 Thurston Ave Ithaca NY USA Independent Researcher Beijing China Master of Computer Science Donald Bren School of Information and Computer Sciences University of California Irvine 510 Aldrich Hall Irvine CA USA School of Informatics Computing and Cyber Systems Northern Arizona University Flagstaff AZ USA Shanghai Institute of Technology Shanghai China

ISBN: (数字)9798350389418

ISBN: (纸本)9798350389425

This paper proposes a deep reinforcement learning based on the warehouse environment, the mobile robot obstacle avoidance Algorithm. Firstly, the value function network is improved based on the pedestrian interaction. We can learn to obtain the relative importance of the current state and the historical trajectory state as well as the joint impact on the robot’s obstacle avoidance strategy. Secondly, the reward function of reinforcement learning is designed based on the spatial behaviour of pedestrians, and the robot is punished for the state where the angle changes too much, so as to achieve the requirement of comfortable obstacle avoidance. Finally, the feasibility and effectiveness of the deep reinforcement learning-based mobile robot obstacle avoidance algorithm in the warehouse environment in the complex environment of the warehouse are verified through simulation experiments.

关键词： Legged locomotion Pedestrians Heuristic algorithms Robot sensing systems Feature extraction Trajectory Safety Data mining Collision avoidance Information technology

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Prediction of energy resolution in the JUNO experiment

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Chinese Physics C 2025年第1期49卷 35-59页

作者： Angel Abusleme Thomas Adam Kai Adamowicz Shakeel Ahmad Rizwan Ahmed Sebastiano Aiello Fengpeng An Qi An Giuseppe Andronico Nikolay Anfimov Vito Antonelli Tatiana Antoshkina Joao Pedro Athayde Marcondes de André Didier Auguste Weidong Bai Nikita Balashov Wander Baldini Andrea Barresi Davide Basilico Eric Baussan Marco Bellato Marco Beretta Antonio Bergnoli Daniel Bick Lukas Bieger Svetlana Biktemerova Thilo Birkenfeld Iwan Blake David Blum Simon Blyth Anastasia Bolshakova Mathieu Bongrand Clément Bordereau Dominique Breton Augusto Brigatti Riccardo Brugnera Riccardo Bruno Antonio Budano Jose Busto Anatael Cabrera Barbara Caccianiga Hao Cai Xiao Cai Yanke Cai Zhiyan Cai Stéphane Callier Steven Calvez Antonio Cammi Agustin Campeny Chuanya Cao Guofu Cao Jun Cao Rossella Caruso Cédric Cerna Vanessa Cerrone Jinfan Chang Yun Chang Auttakit Chatrabhuti Chao Chen Guoming Chen Pingping Chen Shaomin Chen Xin Chen Yiming Chen Yixue Chen Yu Chen Zelin Chen Zhangming Chen Zhiyuan Chen Zikang Chen Jie Cheng Yaping Cheng YuChin Cheng Alexander Chepurnov Alexey Chetverikov Davide Chiesa Pietro Chimenti Yen-Ting Chin Po-Lin Chou Ziliang Chu Artem Chukanov Gérard Claverie Catia Clementi Barbara Clerbaux Marta Colomer Molla Selma Conforti Di Lorenzo Alberto Coppi Daniele Corti Simon Csakli Chenyang Cui Flavio Dal Corso Olivia Dalager Jaydeep Datta Christophe De La Taille Zhi Deng Ziyan Deng Xiaoyu Ding Xuefeng Ding Yayun Ding Bayu Dirgantara Carsten Dittrich Sergey Dmitrievsky Tadeas Dohnal Dmitry Dolzhikov Georgy Donchenko Jianmeng Dong Evgeny Doroshkevich Wei Dou Marcos Dracos Frédéric Druillole ran Du Shuxian Du Katherine Dugas Stefano Dusini Hongyue Duyang Jessica Eck Timo Enqvist Andrea Fabbri Ulrike Fahrendholz Lei Fan Jian Fang Wenxing Fang Dmitry Fedoseev Li-Cheng Feng Qichun Feng Federico Ferraro Amélie Fournier Fritsch Fritsch Haonan Gan Feng Gao Feng Gao Alberto Garfagnini Arsenii Gavrikov Marco Giammarchi Nunzio Giudice Maxim Gonchar Guanghua Gong Hui Gong Yuri Gornushkin Marco Grassi Maxim Gromov Vasily Gromov Minghao G Yerevan Physics Institute YerevanArmenia Université Libre de Bruxelles BrusselsBelgium Universidade Estadual de Londrina LondrinaBrazil Pontificia Universidade Catolica do Rio de Janeiro Rio de JaneiroBrazil Millennium Institute for SubAtomic Physics at the High-energy Frontier(SAPHIR) ANIDChile Pontificia Universidad Católica de Chile SantiagoChile Beijing Institute of Spacecraft Environment Engineering BeijingChina Beijing Normal University BeijingChina China Institute of Atomic Energy BeijingChina Institute of High Energy Physics BeijingChina North China Electric Power University BeijingChina School of Physics Peking UniversityBeijingChina Tsinghua University BeijingChina University of Chinese Academy of Sciences BeijingChina Jilin University ChangchunChina College of Electronic Science and Engineering National University of Defense TechnologyChangshaChina Chongqing University ChongqingChina Dongguan University of Technology DongguanChina Jinan University GuangzhouChina Sun Yat-sen University GuangzhouChina Harbin Institute of Technology HarbinChina University of Science and Technology of China HefeiChina The Radiochemistry and Nuclear Chemistry Group in University of South China HengyangChina Wuyi University JiangmenChina Shandong University JinanChinaand Key Laboratory of Particle Physics and Particle Irradiation of Ministry of EducationShandong UniversityQingdaoChina Nanjing University NanjingChina Guangxi University NanningChina East China University of Science and Technology ShanghaiChina School of Physics and Astronomy Shanghai Jiao Tong UniversityShanghaiChina Tsung-Dao Lee Institute Shanghai Jiao Tong UniversityShanghaiChina Institute of Hydrogeology and Environmental Geology Chinese Academy of Geological SciencesShijiazhuangChina Nankai University TianjinChina Wuhan University WuhanChina Xi'an Jiaotong University Xi'anChina Xiamen University XiamenChina School of Physics and Microelectronics Zhengzhou UniversityZhengzhouChina Institute of Physics National

This paper presents an energy resolution study of the JUNO experiment,incorporating the latest knowledge acquired during the detector construction *** determination of neutrino mass ordering in JUNO requires an exceptional energy resolution better than 3% at 1 *** achieve this ambitious goal,significant efforts have been undertaken in the design and production of the key components of the JUNO *** factors affecting the detection of inverse beta decay signals have an impact on the energy resolution,extending beyond the statistical fluctuations of the detected number of photons,such as the properties of the liquid scintillator,performance of photomultiplier tubes,and the energy reconstruction *** account for these effects,a full JUNO simulation and reconstruction approach is *** enables the modeling of all relevant effects and the evaluation of associated inputs to accurately estimate the energy *** results of this study reveal an energy resolution of 2.95% at 1 ***,this study assesses the contribution of major effects to the overall energy resolution *** analysis serves as a reference for interpreting future measurements of energy resolution during JUNO data ***,it provides a guideline for comprehending the energy resolution characteristics of liquid scintillator-based detectors.

关键词： energy resolution liquid scintillator detector JUNO simulation reconstruction

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Preface

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2025年 14784 LNCS卷 v页

作者： Bai, Guangdong Ishikawa, Fuyuki Aït-Ameur, Yamine Papadopoulos, George A. School of Electrical Engineering and Computer Science Faculty of Engineering Architecture and Information Technology University of Queensland BrisbaneQLD Australia Information Systems Architecture Science Research Division National Institute of Informatics Tokyo Chiyoda-ku Japan IRIT University of Toulouse Toulouse France Department of Computer Science University of Cyprus Nicosia Cyprus

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Towards Non-invasive and Personalized Management of Breast Cancer Patients from Multiparametric MRI via A Large Mixture-of-Modality-Experts Model

arXiv

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arXiv 2024年

作者： Luo, Luyang Wu, Mingxiang Li, Mei Xin, Yi Wang, Qiong Vardhanabhuti, Varut Chu, Winnie C.W. Li, Zhenhui Zhou, Juan rajpurkar, Pranav Chen, Hao Department of Computer Science and Technology The Hong Kong University of Science and Technology Hong Kong Department of Biomedical Informatics Harvard University Boston United States Department of Radiology Shenzhen People's Hospital Shenzhen China Department of Radiology PLA Middle Military Command General Hospital Wuhan China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Department of Diagnostic Radiology Li Ka Shing Faculty of Medicine The University of Hong Kong Hong Kong Department of Imaging and Interventional Radiology The Chinese University of Hong Kong Hong Kong Department of Oncology Yunnan Cancer Hospital Kunming China Department of Radiology 5th Medical Center of Chinese PLA General Hospital Beijing China The Second School of Clinical Medicine Southern Medical University Guangzhou China Department of Chemical and Biological Engineering The Hong Kong University of Science and Technology Hong Kong Division of Life Science The Hong Kong University of Science and Technology Hong Kong

Breast magnetic resonance imaging (MRI) is the imaging technique with the highest sensitivity for detecting breast cancer and is routinely used for women at high risk. Despite the comprehensive multiparametric protocol of breast MRI, existing artificial intelligence-based studies predominantly rely on single sequences and have limited validation. Here we report a large mixture-of-modality-experts model (MOME) that integrates multiparametric MRI information within a unified structure, offering a noninvasive method for personalized breast cancer management. We have curated the largest multiparametric breast MRI dataset, involving 5,205 patients from three hospitals in the north, southeast, and southwest of China, for the development and extensive evaluation of our model. MOME demonstrated accurate and robust identification of breast cancer. It achieved comparable performance for malignancy recognition to that of four senior radiologists and significantly outperformed a junior radiologist, with 0.913 AUROC, 0.948 AUPRC, 0.905 F1 score, and 0.723 MCC. Our findings suggest that MOME could reduce the need for biopsies in BI-raDS 4 patients with a ratio of 7.3%, classify triple-negative breast cancer with an AUROC of 0.709, and predict pathological complete response to neoadjuvant chemotherapy with an AUROC of 0.694. The model further supports scalable and interpretable inference, adapting to missing modalities and providing decision explanations by highlighting lesions and measuring modality contributions. MOME exemplifies a discriminative, robust, scalable, and interpretable multimodal model, paving the way for noninvasive, personalized management of breast cancer patients based on multiparametric breast imaging data. © 2024, CC BY.

关键词： Chemotherapy

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Using CNN with Bayesian optimization to identify cerebral micro-bleeds

Using CNN with Bayesian optimization to identify cerebral mi...

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作者： Doke, Piyush Shrivastava, Dhiraj Pan, Chichun Zhou, Qinghua Zhang, Yu-Dong School of Computer Science and Technology Henan Polytechnic University HenanJiaozuo China School of Mathematics and Computer Science Indian Institute of Technology Goa VelingGoa India Department of Mechanical Engineering Indian Institute of Technology Varanasi VaranasiUttar Pradesh India School of Business Nanjing Normal University NanjingJiangsu China Department of Informatics University of Leicester LeicesterLeicestershire United Kingdom Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia

This article studies the problem of detecting cerebral micro-bleeds (CMBs) using a convolutional neural network (CNN). Cerebral micro-bleeds (CMBs) are increasingly recognized neuroimaging findings, occurring with cerebrovascular diseases, dementia, and normal aging. Naturally enough, it becomes necessary to detect CMBs in the early stages of life. The focus of this article is to infuse new techniques like Bayesian optimization to find the optimum set of hyper-parameters efficiently, making even the simplest of CNN architectures perform well on the problem. Experimentally, we observe our CNN (five layers, i.e., two convolution, two pooling, and one fully connected) achieves accuracy = 98.97%, sensitivity = 99.66%, specificity = 98.14%, and precision = 98.54% on the test set (hold-out validation) when calculated over an average of ten runs. The proposed model outperformed state-of-the-art methods. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Convolutional neural networks

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Generative adversarial network: An overview of theory and applications

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International Journal of Information Management Data Insights 2021年第1期1卷

作者： Aggarwal, Alankrita Mittal, Mamta Battineni, Gopi Department of Computer Science and Engineering Panipat Institute of Engineering and Technology Samalkha 132101 India Department of Computer Science and Engineering G.B. Pant Government Engineering College Okhla New Delhi India Medical Informatics centre School of Medicinal and Health Products Sciences University of Camerino Camerino 62032 Italy

In recent times, image segmentation has been involving everywhere including disease diagnosis to autonomous vehicle driving. In computer vision, this image segmentation is one of the vital works and it is relatively complicated than other vision undertakings as it needs low-level spatial data. Especially, Deep Learning has impacted the field of segmentation incredibly and gave us today different successful models. The deep learning associated Generated Adversarial Networks (GAN) has presenting remarkable outcomes on image segmentation. In this study, the authors have presented a systematic review analysis on recent publications of GAN models and their applications. Three libraries such as Embase (Scopus), WoS, and PubMed have been considered for searching the relevant papers available in this area. Search outcomes have identified 2084 documents, after two-phase screening 52 potential records are included for final review. The following applications of GAN have been emerged: 3D object generation, medicine, pandemics, image processing, face detection, texture transfer, and traffic controlling. Before 2016, research in this field was limited and thereafter its practical usage came into existence worldwide. The present study also envisions the challenges associated with GAN and paves the path for future research in this realm. © 2020

关键词： Big data Deep learning GAN Image mining Literature review Neural networks

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PI-D controller design with finite number of frequency responces model via a ratio of fourier transformations of input and output signals

IEEJ Transactions on Electronics, Information and Systems

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IEEJ Transactions on Electronics, Information and Systems 2019年第4期139卷 402-408页

作者： Kami, Yasushi Tanaka, Ryosuke Mitani, Yuuichiroh Nobuyama, Eitaku Department of Electrical and Computer Engineering National Institute of Technology Akashi College 679-3 Nishioka Uozumi Akashi Hyogo674-8501 Japan Department of Mechanical Engineering National Institute of Technology Numazu College 3600 Ooka Numazu Shizuoka410-8501 Japan Department of Systems Innovation and Informatics Faculty of Computer Science and System Engineering Kyushu Institute of Technology 680-4 Kawazu Iizuka Fukuoka820-8502 Japan

This paper shows a PI-D controller design method with a finite number of frequency responses(FNFR) model so that it stabilizes a closed-loop system and matches a transfer function of the closed-loop system to that of ideal model. In this paper FNFR model is introduced with a ratio of the discrete Fourier transformation of input and output signals. The main contribution of this paper is to propose a method to reduce the control problem to a convex optimization problem. The efficiency of the method with FNFR model is shown through experimental results via a magnetic levitation system © 2019 The institute of Electrical Engineers of Japan.

关键词： Controllers

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Application of artificial intelligence techniques for automated detection of myocardial infarction: A review

arXiv

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arXiv 2021年

作者： Joloudari, Javad Hassannataj Mojrian, Sanaz Nodehi, Issa Mashmool, Amir Zadegan, Zeynab Kiani Shirkharkolaie, Sahar Khanjani Alizadehsani, Roohallah Tamadon, Tahereh Khosravi, Samiyeh Kohnehshari, Mitra Akbari Hassannatajjeloudari, Edris Sharifrazi, Danial Mosavi, Amir Loh, Hui Wen Tan, Ru-San rajendra Acharya, U. Department of Computer Engineering Faculty of Engineering University of Birjand Birjand Iran Department of Information Technology Engineering Mazandaran University of Science and Technology Babol Iran Department of Computer Engineering University of Qom Qom Iran Italy Institute for Intelligent Systems Research and Innovation Deakin University GeelongVIC3216 Australia Computer Engineering Department Engineering Faculty Bu-Ali Sina University Hamedan Iran Department of Nursing School of Nursing and Allied Medical Sciences Maragheh Faculty of Medical Sciences Maragheh Iran Department of Computer Engineering Shiraz Branch Islamic Azad University Shiraz Iran Faculty of Informatics Obuda University Budapest Hungary School of Science and Technology Singapore University of Social Sciences Singapore Department of Cardiology National Heart Centre Singapore Singapore Ngee Ann Polytechnic Singapore599489 Singapore Dept. of Biomedical Informatics and Medical Engineering Asia University Taichung Taiwan

Myocardial infarction (MI) results in heart muscle injury due to receiving insufficient blood flow. MI is the most common cause of mortality in middle-aged and elderly individuals around the world. To diagnose MI, clinicians need to interpret electrocardiography (ECG) signals, which requires expertise and is subject to observer bias. Artificial intelligence-based methods can be utilized to screen for or diagnose MI automatically using ECG signals. In this work, we conducted a comprehensive assessment of artificial intelligence-based approaches for MI detection based on ECG as well as other biophysical signals, including machine learning (ML) and deep learning (DL) models. The performance of traditional ML methods relies on handcrafted features and manual selection of ECG signals, whereas DL models can automate these tasks. The review observed that deep convolutional neural networks (DCNNs) yielded excellent classification performance for MI diagnosis, which explains why they have become prevalent in recent years. To our knowledge, this is the first comprehensive survey of artificial intelligence techniques employed for MI diagnosis using ECG and other biophysical signals. © 2021, CC BY-SA.

关键词： Deep neural networks

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Medical Image Segmentation Review: The Success of U-Net

arXiv

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arXiv 2022年

作者： Azad, Reza Aghdam, Ehsan Khodapanah rauland, Amelie Jia, Yiwei Avval, Atlas Haddadi Bozorgpour, Afshin Karimijafarbigloo, Sanaz Cohen, Joseph Paul Adeli, Ehsan Merhof, Dorit The Institute of Imaging and Computer Vision RWTH Aachen University Aachen52074 Germany The Department of Electrical Engineering Shahid Beheshti University Tehran*** Iran The School of Medicine Mashhad University of Medical Sciences Mashhad*** Iran The Department of Computer Engineering Sharif University of Technology Tehran*** Iran The Center for Artificial Intelligence in Medicine & Imaging Stanford University Palo AltoCA94304 United States Stanford University StanfordCA94305 United States The Faculty of Informatics and Data Science University of Regensburg Regensburg93053 Germany The Fraunhofer Institute for Digital Medicine MEVIS Bremen28359 Germany

Automatic medical image segmentation is a crucial topic in the medical domain and successively a critical counterpart in the computer-aided diagnosis paradigm. U-Net is the most widespread image segmentation architecture due to its flexibility, optimized modular design, and success in all medical image modalities. Over the years, the U-Net model achieved tremendous attention from academic and industrial researchers. Several extensions of this network have been proposed to address the scale and complexity created by medical tasks. Addressing the deficiency of the naive U-Net model is the foremost step for vendors to utilize the proper U-Net variant model for their business. Having a compendium of different variants in one place makes it easier for builders to identify the relevant research. Also, for ML researchers it will help them understand the challenges of the biological tasks that challenge the model. To address this, we discuss the practical aspects of the U-Net model and suggest a taxonomy to categorize each network variant. Moreover, to measure the performance of these strategies in a clinical application, we propose fair evaluations of some unique and famous designs on well-known datasets. We provide a comprehensive implementation library with trained models for future research. In addition, for ease of future studies, we created an online list of U-Net papers with their possible official implementation. All information is gathered in https://***/NITR098/Awesome-U-Net repository. © 2022, CC BY.

关键词： computer aided diagnosis

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Hybrid deep learning methods for phenotype prediction from clinical notes

arXiv

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arXiv 2021年

作者： Khalafi, Sahar Ghadiri, Nasser Moradi, Milad Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan84156-83111 Iran Institute for Artificial Intelligence and Decision Support Center for Medical Statistics Informatics and Intelligent Systems Medical University of Vienna Vienna Austria

MSC Codes 68T50, 68T07Background and objectives: Identifying patient cohorts from clinical notes in secondary electronic health records is a fundamental task in clinical information management. The patient cohort identification needs to identify the patient phenotypes. However, with the growing number of clinical notes, it becomes challenging to analyze the data manually. Therefore, automatic extraction of clinical concepts would be an essential task to identify the patient phenotypes correctly. This paper proposes a novel hybrid model for automatically extracting patient phenotypes using natural language processing and deep learning models to determine the patient phenotypes without dictionaries and human intervention. Methods: The proposed hybrid model is based on a neural bidirectional sequence model (BiLSTM or BiGRU) and a Convolutional Neural Network (CNN) for identifying patient's phenotypes in discharge reports. Furthermore, to extract more features related to each phenotype, an extra CNN layer is run parallel to the hybrid proposed model. The extra CNN layer improves the model in identifying different phenotypes. We used pre-trained embeddings such as FastText and Word2vec separately as the input layers to evaluate other embedding's performance in identifying patient phenotypes. We also measured the effect of applying additional data cleaning steps on discharge reports to identify patient phenotypes by deep learning models. Results: We used discharge reports in the Medical Information Mart for Intensive Care III (MIMIC III) database in our experiments to identify patient phenotypes. Experimental results in internal comparison demonstrate that the proposed model achieved significant performance improvement over existing models. In addition, we pre-trained embeddings using FastText and Word2vce on discharge reports in MIMIC III. The enhanced version of our model with an extra CNN layer obtained a relatively higher F1-score than the original hybrid model. We al

关键词： Embeddings

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