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IEEE Conference on Wireless Communications and Networking

作者： Yongchang Deng Yuze Zou Shimin Gong Bin Lyu Dinh Thai Hoang Dusit Niyato School of Intelligent Systems Engineering Sun Yat-sen University China School of Electronic Information and Communications Huazhong University of Science and Technology China Key Laboratory of Ministry of Education in Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and Telecommunications China School of Electrical and Data Engineering University of Technology Sydney Australia School of Computer Science and Engineering Nanyang Technological University Singapore

In this paper, we consider IRS-assisted transmissions from a multi-antenna access point (AP) to a receiver with uncertain channel information. By adjusting the magnitude of reflecting coefficients, the IRS can sustain its operations by harvesting energy from the AP's signal beamforming. Considering channel estimation errors, we model both the AP-IRS channel and the AP-IRS-receiver as a cascaded channel by norm-based uncertainty sets. This allows us to formulate a robust optimization problem to minimize the AP's transmit power, subject to the receiver's worst-case data rate requirement and the IRS's worst-case power budget constraint. Instead of using the alternating optimization (AO) method, we firstly propose a heuristic scheme to decompose the IRS's phase shift optimization and the AP's active beamforming. Based on semidefinite relaxations of the worst-case constraints, we further devise an iterative algorithm to optimize the AP's transmit beamforming and the magnitude of the IRS's reflecting coefficients efficiently by solving a set of semidefinite programs. Simulation results reveal that the AP requires a higher transmit power to deal with the channel uncertainty. Moreover, the negative effect of channel uncertainty can be alleviated by using a larger-size IRS.

关键词： Wireless communication Uncertainty Array signal processing Simulation Transmitting antennas Channel estimation Receivers

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Distributed Optimal Control for Traffic Networks with Fog Computing

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China Communications 2019年第10期16卷 202-213页

作者： Yijie Wang Lei Wang Saeed Amir Qing-Guo Wang Department of Operations Research at the University of Texas at Austin AustinTexas76019 USA School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191 China Faculty of Engineering and the Built Environment Institute for Intelligent SystemsUniversity of JohannesburgJohannesburg 2006South Africa

This paper presents a distributed optimization strategy for large-scale traffic network based on fog computing. Different from the traditional cloud-based centralized optimization strategy, the fog-based distributed optimization strategy distributes its computing tasks to individual sub-processors, thus significantly reducing computation time. A traffic model is built and a series of communication rules between subsystems are set to ensure that the entire transportation network can be globally optimized while the subsystem is achieving its local optimization. Finally, this paper numerically simulates the operation of the traffic network by mixed-Integer programming, also, compares the advantages and disadvantages of the two optimization strategies.

关键词： fog computing traffic network distributed optimization distributed control

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Enhancement of thermal conductivity and mechanical properties of silicone rubber with oriented fillers connected by covalent bonds

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Composites Part A: Applied science and Manufacturing 2025年 198卷

作者： Liang Zhang Jianhui Qiu Eiichi Sakai Huixia Feng Hong Wu Hiroyuki Yamaguchi Yasunori Chonan Mitsuyoshi Nomura Department of Mechanical Engineering Faculty of Systems Science and Technology Akita Prefectural University Akita 015-0055 Japan College of Petrochemical Technology Lanzhou University of Technology Lanzhou 730050 China The State Key Laboratory of Polymer Materials Engineering Sichuan Provincial Engineering Laboratory of Plastic/Rubber Complex Processing Technology Polymer Research Institute of Sichuan University Chengdu 610065 China Department of Intelligent Mechatronics Faculty of Systems Science and Technology Akita Prefectural University Akita 015-0055 Japan Department of Machine Intelligence and Systems Engineering Akita Prefectural University Akita 015-0055 Japan

High-performance thermal interface materials (TIMs) are pivotal for extending the lifespan and ensuring the reliability of electronic devices, due to their excellent thermal conductivity (TC), flexibility, and electrical insulation properties. This study presents an in-depth analysis of the spatial distribution effects of hybrid fillers and proposes an innovative binary-filler synergistic strategy. By combining the unique characteristics of one-dimensional carbon nanotubes (CNTs) and two-dimensional boron nitride (BN), a vertically aligned silicone rubber/boron nitride/carbon nanotubes (SR/KBN/CNTs) flexible TIM was successfully designed and fabricated using a simple molding process. Surface-modified BN was covalently bonded with CNTs, collaboratively forming a continuous thermally conductive network aligned along the heat flow direction within the SR matrix. This structure significantly reduces interfacial thermal resistance and phonon scattering, thereby effectively enhancing the through-plane TC (K ⊥ ). Specifically, when 50 wt% of the composite filler KBN30/CNTs (KBN: CNTs, 30:1 w/w) was incorporated into the SR matrix, the resulting composite achieved a K ⊥ of 3.57 Wm −1 K −1 , an increase of 1983% compared to pure SR. Moreover, the composite exhibits a fast thermal response, excellent electrical insulation, and robust thermal stability. This combination of properties highlights its great potential for application in the thermal management of future electronic devices.

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A Survey on Temporal Knowledge Graph Completion: Taxonomy, Progress, and Prospects

arXiv

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arXiv 2023年

作者： Wang, Jiapu Wang, Boyue Qiu, Meikang Pan, Shirui Xiong, Bo Liu, Heng Luo, Linhao Liu, Tengfei Hu, Yongli Yin, Baocai Gao, Wen Beijing Municipal Key Lab of Multimedia and Intelligent Software Technology Beijing Artificial Intelligence Institute Faculty of Information Technology Beijing University of Technology Beijing100124 China The Beacom College of Computer and Cyber Sciences Dakota State University MadisonSD United States Griffith University QLD Australia The Department of Computer Science University of Stuttgart Stuttgart70569 Germany The Department of Data Science and AI Monash University Melbourne Australia The Institute of Digital Media Peking University Beijing100871 China The School of Electronic and Computer Engineering Peking University Shenzhen Graduate School Shenzhen518055 China

Temporal characteristics are prominently evident in a substantial volume of knowledge, which underscores the pivotal role of Temporal Knowledge Graphs (TKGs) in both academia and industry. However, TKGs often suffer from incompleteness for three main reasons: the continuous emergence of new knowledge, the weakness of the algorithm for extracting structured information from unstructured data, and the lack of information in the source dataset. Thus, the task of Temporal Knowledge Graph Completion (TKGC) has attracted increasing attention, aiming to predict missing items based on the available information. In this paper, we provide a comprehensive review of TKGC methods and their details. Specifically, this paper mainly consists of three components, namely, 1) Background, which covers the preliminaries of TKGC methods, loss functions required for training, as well as the dataset and evaluation protocol;2) Interpolation, that estimates and predicts the missing elements or set of elements through the relevant available information. It further categorizes related TKGC methods based on how to process temporal information;3) Extrapolation, which typically focuses on continuous TKGs and predicts future events, and then classifies all extrapolation methods based on the algorithms they utilize. We further pinpoint the challenges and discuss future research directions of TKGC. Copyright © 2023, The Authors. All rights reserved.

关键词： Interpolation

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Machine-Learning Methods for Detecting Tuberculosis in Ziehl-Neelsen Stained Slides: A Systematic Literature Review

SSRN

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SSRN 2023年

作者： Tamura, Gabriel Llano, Gonzalo Aristizábal, Andrés Valencia, Juan Sua, Luz Fernanda Fernandez, Liliana Department of Computing and Intelligent Systems Universidad Icesi Valle del Cauca Cali Colombia Center of Artificial Intelligence and Data Science Universidad Icesi Valle del Cauca Cali Colombia Universidad Icesi Valle del Cauca Cali Colombia Faculty of Health Sciences Universidad Icesi Valle del Cauca Cali Colombia Department of Pathology and Laboratory Medicine Fundación Valle del Lili Valle del Cauca Cali Colombia Department of Internal Medicine Pulmonology Service Fundación Valle del Lili Valle del Cauca Cali Colombia

In recent years, different Machine Learning (ML) methods applied to medical image analysis have been increasingly used for the detection of Mycobacterium Tuberculosis (MT) in Ziehl-Neelsen stained human samples, since manual detection and counting of bacteria is a tedious, costly, error-prone and slow task. This paper presents the results of a Systematic Literature Review (SLR) that comprehensively surveyed and analyzed the current stateof-the-art ML methods and approaches that have been used for TB detection in the period of 2017-2022, as well as their achieved metrics and the characteristics of the datasets on which they were applied. The primary goal of this⋆ This work was supported by Tecnoquímicas Corp. in partnertship with Fundación Valle del Lili and Universidad Icesi with the aim to strenghten the research and development of technology in health in the Valle del Cauca region. © 2023, The Authors. All rights reserved.

关键词： Computer vision

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Pseudoinverse-free Zhang neurodynamics for temporally-variant nonlinear equation system solving applied to robot manipulator

引用

Neurocomputing 2025年

作者： Meichun Huang Yunong Zhang Shuai Li School of Computer Science and Engineering Sun Yat-sen University Guangzhou 510006 China School of Intelligent Systems Engineering Sun Yat-sen University Shenzhen 518107 China Faculty of Information Technology and Electrical Engineering University of Oulu Oulu 90570 Finland VTT-Technology Research Center of Finland Oulu 90570 Finland

The solution of temporally-variant nonlinear equation system (TVNES) is regarded as a challenging dynamics problem with extensive applications across various fields, especially in control. In this paper, the TVNES problem is explored and solved from the perspective of control. Zhang neurodynamics (ZN) approach is used extensively for solving the TVNES problem because it is well equipped with time-derivative information. However, traditional ZN controller inevitably performs the pseudoinverse operation of temporally-variant Jacobian matrix, which has high computational complexity. Different from previous studies, a novel pseudoinverse-free ZN (PFZN) controller is proposed in this paper, which eliminates the pseudoinverse operation of temporally-variant Jacobian matrix, as a result reducing the computational complexity. Additionally, the convergence performance of the PFZN controller and the robustness of the disturbed PFZN controller are verified through theoretical analyses. Furthermore, numerical and comparative experiments show the validity of the theoretical analyses and the superiority of the PFZN controller over the gradient neurodynamics, gradient ZN, and varying-parameter ZN controllers. Finally, the path-following control problem of the UR5 robot manipulator is efficiently solved by the PFZN controller, which validates the effectiveness and applicability of the proposed PFZN controller.

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Statistical analysis into the drivers behind relativistic electron precipitation events observed by CALET on the International Space Station 38

Statistical analysis into the drivers behind relativistic el...

引用

38th International Cosmic Ray Conference, ICRC 2023

作者： Ficklin, A.W. Bruno, A. Blum, L. de Nolfo, G.A. Guzik, T.G. Kataoka, R. Remya, B. Vidal-Luengo, S. Adriani, O. Akaike, Y. Asano, K. Asaoka, Y. Berti, E. Bigongiari, G. Binns, W.R. Bongi, M. Brogi, P. Bruno, A. Cannady, N. Castellini, G. Checchia, C. Cherry, M.L. Collazuol, G. de Nolfo, G.A. Ebisawa, K. Fuke, H. Gonzi, S. Hams, T. Hibino, K. Ichimura, M. Ioka, K. Ishizaki, W. Israel, M.H. Kasahara, K. Kataoka, J. Katayose, Y. Kato, C. Kawanaka, N. Kawakubo, Y. Kobayashi, K. Kohri, K. Krawczynski, H.S. Krizmanic, J.F. Maestro, P. Marrocchesi, P.S. Messineo, A.M. Mitchell, J.W. Miyake, S. Moiseev, A.A. Mori, M. Mori, N. Motz, H.M. Munakata, K. Nakahira, S. Nishimura, J. Okuno, S. Ormes, J.F. Ozawa, S. Pacini, L. Papini, P. Rauch, B.F. Ricciarini, S.B. Sakai, K. Sakamoto, T. Sasaki, M. Shimizu, Y. Shiomi, A. Spillantini, P. Stolzi, F. Sugita, S. Sulaj, A. Takita, M. Tamura, T. Terasawa, T. Torii, S. Tsunesada, Y. Uchihori, Y. Vannuccini, E. Wefel, J.P. Yamaoka, K. Yanagita, S. Yoshida, A. Yoshida, K. Zober, W.V. Department of Physics University of Florence Via Sansone 1 Sesto Fiorentino 50019 Italy INFN Sezione di Firenze Via Sansone 1 Sesto Fiorentino 50019 Italy Waseda Research Institute for Science and Engineering Waseda University 17 Kikuicho Shinjuku Tokyo162-0044 Japan JEM Utilization Center Human Spaceflight Technology Directorate Japan Aerospace Exploration Agency 2-1-1 Sengen Ibaraki Tsukuba305-8505 Japan Institute for Cosmic Ray Research The University of Tokyo 5-1-5 Kashiwa-no-Ha Kashiwa Chiba277-8582 Japan Via Madonna del Piano 10 Sesto Fiorentino 50019 Italy Department of Physical Sciences Earth and Environment University of Siena via Roma 56 Siena53100 Italy INFN Sezione di Pisa Polo Fibonacci Largo B. Pontecorvo 3 Pisa56127 Italy Department of Physics McDonnell Center for the Space Sciences Washington University One Brookings Drive St. LouisMO63130-4899 United States Heliospheric Physics Laboratory NASA GSFC GreenbeltMD20771 United States Center for Space Sciences and Technology University of Maryland Baltimore County 1000 Hilltop Circle BaltimoreMD21250 United States Astroparticle Physics Laboratory NASA GSFC GreenbeltMD20771 United States Center for Research and Exploration in Space Sciences and Technology NASA GSFC GreenbeltMD20771 United States Department of Physics and Astronomy Louisiana State University 202 Nicholson Hall Baton RougeLA70803 United States Department of Physics and Astronomy University of Padova Via Marzolo 8 Padova35131 Italy INFN Sezione di Padova Via Marzolo 8 Padova35131 Italy Institute of Space and Astronautical Science Japan Aerospace Exploration Agency 3-1-1 Yoshinodai Chuo Kanagawa Sagamihara252-5210 Japan Kanagawa University 3-27-1 Rokkakubashi Kanagawa Yokohama221-8686 Japan Faculty of Science and Technology Graduate School of Science and Technology Hirosaki University 3 Bunkyo Hirosaki Aomori036-8561 Japan Yukawa Institute for Theoretical Physics Kyoto Uni

The Calorimetric Electron Telescope (CALET), launched to the International Space Station in 2015, provides more than 7 years of continuous observation of the radiation environment at low earth orbit. Using this dataset, we present a method for the detection and categorization of MeV relativistic electron precipitation (REP) events. From this catalog we identify a subset of a few hundred REP events observed at times where CALET is in magnetic conjunction with the Van Allen probes. These conjugate measurements enable studies of associated plasma wave data from RBSPA/B and potential drivers for MeV electron precipitation. We show that roughly 10 percent of the observed REP events are associated with enhanced electromagnetic ion cyclotron wave activity, suggesting that waves can play a significant role in driving MeV electron precipitation. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Cyclotron radiation

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Importance of Wearable Health Monitoring systems Using IoMT;Requirements, Advantages, Disadvantages and Challenges

Importance of Wearable Health Monitoring Systems Using IoMT;...

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International Symposium on Computational Intelligence and Informatics

作者： Fahime Khozeimeh Mohamad Roshanzamir Afshin Shoeibi Mohammad Tayarani Darbandy Roohallah Alizadehsani Hamid Alinejad-Rokny Davood Ahmadian Abbas Khosravi Saeid Nahavandi Institute for Intelligent Systems Research and Innovation (IISRI) Deakin University Victoria Australia Department of Computer Engineering Fasa University Fasa Iran Internship in Health Data Analytics Program AI-enabled Processes (AIP) Research Centre Macquarie University Sydney Australia School of Architecture Islamic Azad University Taft Taft Iran BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney Sydney NSW Australia Faculty of Mathematics Statistics and Computer Sciences University of Tabriz Tabriz Iran

ISBN: (纸本)9798350398830

Providing better quality of service is one of the most important goals in medical/healthcare systems. The services provided should have features such as low latency, appropriate geographic distribution, and real-time interaction. Internet of Medical Things (IoMT) technology is one of the solutions to achieve high quality of services with these features. The wearable health monitoring systems which have become popular in the last decade are one of the foundations of achievement to these features. In this paper, the importance of wearable devices under IoMT technology is investigated. There are several reasons for using wearable devices under IoMT technology, such as reducing healthcare costs and long waiting times. Therefore, anyone working on wearable devices needs to have a general understanding of how they are designed and function. In fact, the integration of new devices to monitor and alert patients about their physical health is a promising option for improving health care systems around the world. Overcrowding in medical centers will be prevented by upgrading the facilities for the virtual presence of patients. It can also ensure real-time monitoring and reduce operating costs. In addition, it can accelerate treatment of patients and reduce the cost of investment and increase its coverage.

关键词： Costs Medical devices Wearable computers Quality of service Organizations Real-time systems Security

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Neural Coding Strategies for Event-Based Vision data

Neural Coding Strategies for Event-Based Vision Data

引用

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Shane Harrigan Sonya Coleman Dermot Kerr Pratheepan Yogarajah Zheng Fang Chengdong Wu Intelligent Systems Research Centre Ulster University Northern Ireland United Kingdom Faculty of Robot Science and Engineering Northeastern University China

ISBN: (数字)9781509066315

ISBN: (纸本)9781509066322

Neural coding schemes are powerful tools used within neuroscience. This paper introduces three different neural coding scheme formations for event-based vision data which are designed to emulate the neural behaviour exhibited by neurons under stimuli. Presented are phase-of-firing and two sparse neural coding schemes. It is determined that machine learning approaches, i.e. Convolutional Neural Network combined with a Stacked Autoencoder network, produce powerful descriptors of the patterns within events. These coding schemes are deployed in an existing action recognition template and evaluated using two popular event-based data sets.

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Semantics-Guided Contrastive Network for Zero-Shot Object Detection

arXiv

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arXiv 2021年

作者： Yan, Caixia Chang, Xiaojun Luo, Minnan Liu, Huan Zhang, Xiaoqin Zheng, Qinghua The Ministry of Education Key Lab of Intelligent Networks and Network Security National Engineering Lab for Big Data Analytics School of Electronic and Information Engineering Xi'an Jiaotong University Shaanxi710049 China The ReLER Lab AAAI Faculty of Engineering and Information Technology University of Technology Sydney Australia The College of Computer Science and Artificial Intelligence Wenzhou University China

Zero-shot object detection (ZSD), the task that extends conventional detection models to detecting objects from unseen categories, has emerged as a new challenge in computer vision. Most existing approaches tackle the ZSD task with a strict mapping-transfer strategy that may lead to suboptimal ZSD results: 1) the learning process of these models neglects the available semantic information on unseen classes, which can easily bias towards the seen categories;2) the original visual feature space is not well-structured for the ZSD task due to the lack of discriminative information. To address these issues, we develop a novel Semantics-Guided Contrastive Network for ZSD, named ContrastZSD, a detection framework that first brings contrastive learning mechanism into the realm of zero-shot detection. Particularly, ContrastZSD incorporates two semantics-guided contrastive learning subnets that contrast between region-category and region-region pairs respectively. The pairwise contrastive tasks take advantage of supervision signals derived from both the ground truth label and class similarity information. By performing supervised contrastive learning over those explicit semantic supervision, the model can learn more knowledge about unseen categories to avoid the bias problem to seen concepts, while optimizing the visual data structure to be more discriminative for better visual-semantic alignment. Extensive experiments are conducted on two popular benchmarks for ZSD, i.e., PASCAL VOC and MS COCO. Results show that our method outperforms the previous state-of-the-art on both ZSD and generalized ZSD tasks. © 2021, CC BY.

关键词： Object detection

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