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Advances in Intelligent Systems and Computing 2021年 1185卷 v-vi页

作者： Wu, C.H. Patnaik, Srikanta Popentiu Vlãdicescu, Florin Nakamatsu, Kazumi ShatinNew Territories Hong Kong Department of Computer Science and Engineering Faculty of Engineering and Technology Siksha ‘O’ Anusandhan University BhubaneswarOdisha India Information and Communication Engineering Faculty Automatic Control and Computer Science University of Oradea Oradea Romania School of Human Science and Environment University of Hyogo Himeji Japan

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Artificial intelligence enabled radio propagation for communications - Part I: Channel characterization and antenna-channel optimization

arXiv

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arXiv 2021年

作者： Huang, Chen He, Ruisi Ai, Bo Molisch, Andreas F. Lau, Buon Kiong Haneda, Katsuyuki Liu, Bo Wang, Cheng-Xiang Yang, Mi Oestges, Claude Zhong, Zhangdui The Purple Mountain Laboratories Nanjing211111 China The National Mobile Communications Research Laboratory School of Information Science and Engineering Southeast University Nanjing210096 China The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China The Key Laboratory of Railway Industry of Broadband Mobile Information Communications Beijing Jiaotong University Beijing100044 China The School of Information Engineering Zhengzhou University Zhengzhou450001 China Peng Cheng Laboratory Shenzhen518055 China The Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los AngelesCA90089 United States The Department of Electrical and Information Technology Lund University Lund22100 Sweden The Department of Radio Science and Engineering Aalto University Aalto00076 Finland The School of Engineering University of Glasgow GlasgowG12 8QQ United Kingdom The Purple Mountain Laboratories Nanjing211111 China The Institute of Information and Communication Technologies Electronics and Applied Mathematics Universite Catholique de Louvain Louvain-la-Neuve 1348 Belgium

To provide higher data rates, as well as better coverage, cost efficiency, security, adaptability, and scalability, the 5G and beyond 5G networks are developed with various artificial intelligence techniques. In this two-part paper, we investigate the application of artificial intelligence (AI) and in particular machine learning (ML) to the study of wireless propagation channels. It firstly provides a comprehensive overview of ML for channel characterization and ML-based antenna-channel optimization in this first part, and then it gives a state-of-the-art literature review of channel scenario identification and channel modeling in Part II. Fundamental results and key concepts of ML for communication networks are presented, and widely used ML methods for channel data processing, propagation channel estimation, and characterization are analyzed and compared. A discussion of challenges and future research directions for ML-enabled next generation networks of the topics covered in this part rounds off the paper. © 2021, CC BY-NC-ND.

关键词： Machine learning

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Sparse Representation De-noising for MT Signal Combined with Particle Swarm Optimization

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Acta Geologica Sinica - English Edition 2019年第S1期93卷

作者： Xiaoqiong Liu Guang Li Tingtian Tang Jin Li College of Information Science and Engineering Hunan Normal University Changsha China School of Geophysics and Measurement-control Technology East China University of Technology Nanchang China School of Geosciences and Info-Physics Central South University Changsha 410083 China Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring (Central South University) Ministry of Education Changsha China

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Characterising acute and chronic care needs: insights from the Global Burden of Disease Study 2019

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Nature communications 2025年第1期16卷 4235-4235页

作者： Cecilia Anza-Ramirez J. Jaime Miranda Benedetta Armocida Jorge César Correia Harriette Gillian Christine Van Spall David Beran Amirali Aali Kalkidan Hassen Abate Semagn Mekonnen Abate Cristiana Abbafati Mohsen Abbasi-Kangevari Zeinab Abbasi-Kangevari Zahra Aryan Sina Azadnajafabad Mohammadreza Azangou-Khyavy Seyyed-Hadi Ghamari Mohammad Keykhaei Javad Khanali Sara Momtazmanesh Fateme Montazeri Shayan Rahmani Mohammad-Mahdi Rashidi Nazila Rezaei Negar Rezaei Sahar Saeedi Moghaddam Hossein Hassanian-Moghaddam Ali-Asghar Kolahi Ali Nikoobar Hedayat Abbastabar Ahmed M. Abdel-Azeem Michael Abdelmasseh Juan Sanabria Sherief Abd-Elsalam Ahmed Abdelwahab Gholamreza Abdoli Mohammad Abdollahi Meriem Abdoun Deldar Morad Abdulah Abu Yousuf Md Abdullah Ame Mehadi Abdurehman Getachew Abebe Aidin Abedi Chirag P. Doshi Vida Abedi Hassan Abidi Richard Gyan Aboagye Hassan Abolhassani Nima Rezaei Lucas Guimarães Abreu Michael R. M. Abrigo Yonas Derso Abtew Hiwa Abubaker Ali Eman Abu-Gharbieh Hiba Jawdat Barqawi Rabih Halwani Maha Mohamed Saber-Ayad Iman M. Talaat Ahmed Abu-Zaid Manfred Mario Kokou Accrombessi Juan Manuel Acuna Denberu Eshetie Adane Tigist Demssew Adane Isaac Yeboah Addo Giovanni Addolorato Oluwafemi Atanda Adeagbo Victor Adekanmbi Miracle Ayomikun Adesina Isaac Iyinoluwa Olufadewa Olatunji O. Adetokunboh Daniel Adedayo Adeyinka Qorinah Estiningtyas Sakilah Adnani Aanuoluwapo Adeyimika Afolabi Olayinka Stephen Ilesanmi Muhammad Sohail Afzal Saira Afzal Gina Agarwal Prerna Agarwal Pradyumna Agasthi Anurag Agrawal Marcela Agudelo-Botero Adolfo Martinez-Valle Bright Opoku Ahinkorah Aqeel Ahmad Sajjad Ahmad Sohail Ahmad Tauseef Ahmad Ali Ahmadi Siamak Sabour Keivan Ahmadi Sanjay Basu Sepideh Ahmadi Ali Ahmed Shaun Wen Huey Lee Ayman Ahmed Haroon Ahmed Jivan Qasim Ahmed Gahin Abdulraheem Tayib Yahya Yahya Luai A. Ahmed Iffat Elbarazi Syed Mahboob Shah Janardhana P. Aithala Marjan Ajami Budi Aji Hossein Akbarialiabad Yibeltal Akelew Meisam Akhlaghdoust Elham Jamshidi Addis Aklilu Azene Tesfaye Melat Woldemariam Maxwell Ak CRONICAS Center of Excellence in Chronic Diseases Universidad Peruana Cayetano Heredia Lima Peru School of Medicine Universidad Peruana Cayetano Heredia Lima Peru Sydney School of Public Health Faculty of Medicine and Health University of Sydney Sydney NSW Australia Faculty of Epidemiology and Population Health London School of Hygiene & Tropical Medicine London UK Division of Tropical and Humanitarian Medicine University of Geneva Geneva Switzerland Department of Cardiovascular Endocrine-metabolic Diseases and Aging Istituto Superiore di Sanità Rome Italy Division of Endocrinology Diabetology Nutrition and Therapeutic Patient Education Department of Medicine Geneva University Hospitals Geneva Switzerland Department of Medicine and Department of Health Research Evidence and Impact McMaster University Hamilton ON Canada Research Institute of St. Joe’s Hamilton ON Canada Population Health Research Institute Hamilton Canada Geneva University Hospitals Geneva Switzerland Faculty of Medicine Mashhad University of Medical Sciences Mashhad Iran Department of Nutrition and Dietetics Jimma University Jimma Ethiopia Anesthesiology Department Dilla University Addis Ababa Ethiopia Department of Juridical and Economic Studies La Sapienza University Rome Italy Non-Communicable Diseases Research Center Tehran University of Medical Sciences Tehran Iran Social Determinants of Health Research Center Shahid Beheshti University of Medical Sciences Tehran Iran Brigham and Women’s Hospital Harvard University Boston MA USA Students’ Scientific Research Center (SSRC) Tehran University of Medical Sciences Tehran Iran School of Medicine Tehran University of Medical Sciences Tehran Iran School of Medicine Shahid Beheshti University of Medical Sciences Tehran Iran Endocrinology and Metabolism Research Institute Tehran University of Medical Sciences Tehran Iran Chapter of Addiction Medicine University of Sydney Sydney NSW Australia Advanced Diagnostic and Intervent

Chronic care manages long-term, progressive conditions, while acute care addresses short-term conditions. Chronic conditions increasingly strain health systems, which are often unprepared for these demands. This study examines the burden of conditions requiring acute versus chronic care, including sequelae. Conditions and sequelae from the Global Burden of Diseases Study 2019 were classified into acute or chronic care categories. Data were analysed by age, sex, and socio-demographic index, presenting total numbers and contributions to burden metrics such as Disability-Adjusted Life Years (DALYs), Years Lived with Disability (YLD), and Years of Life Lost (YLL). Approximately 68% of DALYs were attributed to chronic care, while 27% were due to acute care. Chronic care needs increased with age, representing 86% of YLDs and 71% of YLLs, and accounting for 93% of YLDs from sequelae. These findings highlight that chronic care needs far exceed acute care needs globally, necessitating health systems to adapt accordingly. © 2025. The Author(s).

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Microseismic source location using a 3D velocity model: From the ray tracing method to waveform inversion

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IOP Conference Series: Earth and Environmental Science 2021年第4期861卷

作者： Xueyi Shang Runxue Miao Yi Wang State Key Laboratory of Coal Mine Disaster Dynamics and Control School of Resources and Safety Engineering Chongqing University Chongqing 400044 China State Key Laboratory of Geodesy and Earth's Dynamics Innovation Academy for Precision Measurement Science and Technology Chinese Academy of Sciences Wuhan 430077 China

Microseismic (MS) source location is important in MS monitoring, providing the basis for determining fracture zones and calculating seismic source parameters (e.g., event magnitude and focal mechanisms). To date, homogenous, 1D, and simple 3D velocity models have been adopted in MS source location. However, a mine is usually characterized by a strong velocity heterogeneity due to engineering geology, 3D geostress, and excavation. In this work, we adopted a travel-time tomography-based high-resolution 3D velocity model and then applied 3D ray tracing based on the shooting method, 3D Gaussian beam-based reverse-time method, and waveform inversion method to MS source location. A semiautomatic waveform-cut method based on the cross-correlation (WCC) technique was developed for a quick, robust, and precise determination of the direct P-phase relative delay times. Additionally, the spectral element method for wavefield modeling, multiscale grid (coarse grid + fine grid) 3D waveform inversion, and L-BGFS iterative method were applied in the waveform inversion. Our results show that the commonly used ray tracing method may be affected by multiray path effects and waveform focusing/defocusing during wavefield propagation, whereas the Gaussian beam method has a frequency-dependent width, and the waveform inversion method has a broader frequency width, which can effectively overcome the issues of ray tracing. The average location errors of eight blasting events obtained using the 3D ray tracing, Gaussian beam, and waveform inversion methods are 26.2, 17.0, and 17.6 m, respectively, which are smaller than those of previous researches obtained using the homogenous-velocity model (average location error > 40 m). In conclusion, the high resolution of the location methods based on 3D velocity models can provide an effective approach to improve the MS source location accuracy and exhibit broad application potential.

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Correction to: Five-axis flank milling stability prediction by considering the tool-workpiece interactions and speed effect

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The International Journal of Advanced Manufacturing technology 2020年第7期108卷 2061-2061页

作者： Yongjian Ji Xibin Wang Zhibing Liu Hongjun Wang Wei Feng Yong Wang Shuyao Liu Beijing Key Laboratory of Measurement & Control of Mechanical and Electrical System Technology Beijing Information Science & Technology University Beijing People’s Republic of China Key Laboratory of Fundamental Science for Advanced Machining Beijing Institute of Technology Beijing People’s Republic of China School of Mechanical and Electronic Engineering Henan University of Technology High-tech District Zhengzhou People’s Republic of China

This original article contained a mistake.

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Privacy Protection of the Smart Grid System Based on Blockchain

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Journal of Physics: Conference Series 2021年第2期1744卷

作者： Siyuan Liu Qiong Zhang Honggang Liu School of Control and Computer Engineering North China Electric Power University Baoding China Guodian NARI Technology Co. Ltd. Nanjing Jiangsu Province China Information and Communication Branch of Chongqing Electric Power Company of State Power Grid Chongqing China

With the development of new technologies for smart grid construction, the smart grid is gradually developing. Smart grid construction is an important field of next generation information technology, and its value and significance have been widely recognized. Smart grid construction not only provides development opportunities, but also brings great challenges. Security and privacy have become serious problems in the development of smart grid. Based on this, this paper discusses the system structure of the block chain, and puts forward the security diagnosis scheme of the smart grid according to the existing problems in the smart grid. Based on the theoretical basis of smart grid big data technology, this paper combines the grid data encryption process with the privacy protection process under the premise of protecting privacy and security, and develops the latest network security protocol. Then use block chain distributed storage to reduce the connection protection process in privacy, and use multi-level encryption. The experimental results show that the technology can improve the rate of sensitive data hiding and has good anti-cracking ability, which provides a theoretical reference for the construction of enterprise power grid.

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CPU utilization prediction method based on composite model

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Journal of Physics: Conference Series 2020年第1期1693卷

作者： Benran Hu Wuzhuo Peng Yanjun Li Jiyuan Ren Yiqun Li Information & Communication Branch Heilongjiang Electric Power Company (LTD) SGCC Harbin 150090 China School of Electrical and Electronic Engineering Harbin University of Science and Technology Harbin 150080 China Power Dispatching & Control Center SGCC Northeast China Branch Shenyang 110180 China

The business system server under the big data environment is always faced with a large number of data services, which makes the performance of its core hardware CPU face a serious test. Therefore, the prediction of server CPU utilization is of great significance in server resource allocation. In response to this situation, a method of server CPU utilization prediction based on ARMA-BiLSTM composite model is proposed. In this method, the original CPU utilization data is decomposed by wavelet decomposition, and the trend item and the detail item of CPU utilization data are obtained. Based on the Bi-LSTM neural network model, the trend item is modeled and predicted, and the detail item is modeled and predicted using the ARMA model. The prediction results of the two are added together to obtain the final prediction result. Through experimental verification, the proposed prediction model has higher accuracy than traditional models.

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REDUCTION METHOD OF TRACKING ERRORS DUE TO MULTIPATH WAVES IN DIRECT SEQUENCE SPREAD SPECTRUM SYSTEMS.

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Electronics and communications in Japan, Part I: communications (English translation of Denshi Tsushin Gakkai Ronbunshi) 1987年第9期70卷 118-126页

作者： Maruyama, Satoshi Iwanami, Yasunori Ikeda, Tetsuo Faculty of Engineering Nagoya Institute of Technology Nagoya Japan 486 Satoshi Maruyama graduated 1983 Dept. Electrical Eng. Fac. Eng. Nagoya Inst. Technology. Completed Master's program 1985 Grad. School and affiliated with Fujitsu Co. Engaged in researches in distance measurement by spread spectrum technique. Tetsuo Ikeda graduated 1961 Dept. Comm. Eng. Fac. Eng. Tohoku University. Completed doctoral program 1966 Grad. School. Assistant 1966 and Assoc. Prof. 1968 Fac. Eng. Tohoku University. Assoc. Prof. 1973 and Prof. 1977 Nagoya Inst. Technology. Engaged in researches in network theory computer networks communication system and electromagnetic environment. Doctor of Eng. Member I.E.E.E. Japan TV Soc. Jap. Inf. Proc. Soc. Jap. Soc. Inf. Comm. and I.E.E.E.

The spread spectrum (SS) communication is a communication system which has excellent characteristics against noise and is suited to the code-division multiplex. This paper describes a method which reduces the tracking phase error in DLL due to multipath waves in SS communication system. The method is based on the model with a single multipath wave.

关键词： RADIO TRANSMISSION

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Multiple residual dense networks for reconfigurable intelligent surfaces cascaded channel estimation

arXiv

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arXiv 2021年

作者： Jin, Yu Zhang, Jiayi Huang, Chongwen Yang, Liang Xiao, Huahua Ai, Bo Wang, Zhiqin The School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China The Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong University Beijing100044 China Zhejiang Provincial Key Lab of Information Processing Communication and Networking Zhejiang University Hangzhou310007 China College of Computer Science and Electronic Engineering Hunan University Changsha410082 China ZTE Corporation State Key Laboratory of Mobile Network Mobile Multimedia Technology Shenzhen518057 China State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China Henan Joint International Research Laboratory of Intelligent Networking and Data Analysis Zhengzhou University Zhengzhou450001 China Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen518055 China China Academy of Information and Communications Technology Beijing100191 China

Reconfigurable intelligent surface (RIS) constitutes an essential and promising paradigm that relies programmable wireless environment and provides capability for space-intensive communications, due to the use of low-cost massive reflecting elements over the entire surfaces of man-made structures. However, accurate channel estimation is a fundamental technical prerequisite to achieve the huge performance gains from RIS. By leveraging the low rank structure of RIS channels, three practical residual neural networks, named convolutional blind denoising network, convolutional denoising generative adversarial networks and multiple residual dense network, are proposed to obtain accurate channel state information, which can reflect the impact of different methods on the estimation performance. Simulation results reveal the evolution direction of these three methods and reveal their superior performance compared with existing benchmark schemes. Copyright © 2021, The Authors. All rights reserved.

关键词： Channel estimation

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