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arXiv 2024年

作者： Luo, Zijin Wang, Xu Wang, Yiquan Zhang, Haotian Li, Zhuangzhuang College of Mathematics Jilin University Jilin Changchun130000 China College of Communication Engineering Jilin University Jilin Changchun130000 China College of Mathematics and System Science Xinjiang University Urumqi Xinjiang830046 China College of Computer Science and Technology Jilin University Jilin Changchun130000 China Department of Electronic Engineering Tsinghua University Beijing100084 China Shenzhen X-Institute Shenzhen518055 China

In order to enhance students’ initiative and participation in MOOC learning, this study constructed a multi-level network model based on Social Network Analysis (SNA). The model makes use of data pertaining to nearly 40,000 users and tens of thousands of courses from various higher education MOOC platforms. Furthermore, an AI-based assistant has been developed which utilises the collected data to provide personalised recommendations regarding courses and study groups for students. The objective is to examine the relationship between students’ course selection preferences and their academic interest levels. Based on the results of the relationship analysis, the AI assistant employs technologies such as GNN to recommend suitable courses and study groups to students. This study offers new insights into the potential of personalised teaching on MOOC platforms, demonstrating the value of data-driven and AI-assisted methods in improving the quality of online learning experiences, increasing student engagement, and enhancing learning outcomes. © 2024, CC BY-NC-SA.

关键词： Contrastive Learning

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Short-term train arrival delay prediction:a data-driven approach

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Railway Sciences 2024年第4期3卷 514-529页

作者： Qingyun Fu Shuxin Ding Tao Zhang Rongsheng Wang Ping Hu Cunlai Pu Postgraduate Department China Academy of Railway SciencesBeijingChina Signal and Communication Research Institute China Academy of Railway Sciences Corporation LimitedBeijingChina Traffic Management Laboratory for High-Speed Railway National Engineering Research Center of System Technology for High-Speed Railway and Urban Rail TransitBeijingChina Scientific and Technological Information Research Institute China Academy of Railway Sciences Corporation LimitedBeijingChina Yibin Track Signal and Communication DepotChina Railway Chengdu Group Co.LtdChengduChina School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjingChina

Purpose-To optimize train operations,dispatchers currently rely on experience for quick adjustments when delays ***,delay predictions often involve imprecise shifts based on known delay ***-time and accurate train delay predictions,facilitated by data-driven neural network models,can significantly reduce dispatcher stress and improve adjustment *** current train operation data,these models enable swift and precise predictions,addressing challenges posed by train delays in high-speed rail networks during unforeseen ***/methodology/approach-This paper proposes CBLA-net,a neural network architecture for predicting late arrival *** combines CNN,Bi-LSTM,and attention mechanisms to extract features,handle time series data,and enhance information *** on operational data from the Beijing-Tianjin line,it predicts the late arrival time of a target train at the next station using multidimensional input data from the target and preceding ***-This study evaluates our model’s predictive performance using two data approaches:one considering full data and another focusing only on late *** show precise and rapid *** with full data achieves aMAEof approximately 0.54 minutes and a RMSEof 0.65 minutes,surpassing the model trained solely on delay data(MAE:is about 1.02 min,RMSE:is about 1.52 min).Despite superior overall performance with full data,the model excels at predicting delays exceeding 15 minutes when trained exclusively on late *** enhanced adaptability to real-world train operations,training with full data is ***/value-This paper introduces a novel neural network model,CBLA-net,for predicting train delay *** innovatively compares and analyzes the model’s performance using both full data and delay data ***,the evaluation of the network’s predictive capabilities considers different scenarios,providing a comprehensive demonstra

关键词： Train delay prediction Intelligent dispatching command Deep learning Convolutional neural network Long short-term memory Attention mechanism

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Multi-objective discrete strength pareto evolutionary algorithm II for multiple-product partial U-shaped disassembly line balancing problem 2

Multi-objective discrete strength pareto evolutionary algori...

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2nd International Conference on computer Vision and Data Mining, ICVDM 2021

作者： Lu, FaYang Liu, Peisheng Qi, Liang Qin, Shujin Xu, Gongdan Xu, Zhiying College of Computer and Communication Engineering Liaoning Petrochemical University Fushun113001 China Department of Intelligent Sci. and Tech. Shandong University of Science and Technology Qingdao266590 China College of Economics and Management Shangqiu Normal University Shangqiu476000 China Institute of System Engineering Macao University of Science and Technology China Department of Biochemistry Engineering Chaoyang Teachers College Chaoyang122000 China

In disassembly industry, the disassembly efficiency closely depends on a reasonable disassembly mode and an efficient disassembly line. In the current manufacturing, the profit of multi-product partial U-shaped disassembly-line-balancing (MPUD) is relatively low and the energy consumption is high. According to the characteristics of the MPUD problem, a mathematical model for maximizing profit and minimizing energy consumption of human-robot disassembly is established. To solve the MPUD problem, a multi-objective strength Pareto evolutionary algorithm II is developed and crossover and mutation operators are improved to promote the convergence and feasibility of the algorithm. We perform the experiments on a ballpoint pen and a washing machine to simulate the disassembly process and compare the proposed algorithm with the nondominated sorting genetic algorithm II and the multi-objective evolutionary algorithm based on decomposition. Experimental results show that the proposed algorithm has obvious advantages in the most important metrics. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Profitability

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Deep Learning in Smart Healthcare: A GAN-based Approach for Imbalanced Alzheimer's Disease Classification

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Procedia computer Science 2024年 241卷 146-153页

作者： Hina Tufail Abdul Ahad Ira Puspitasari Ibraheem Shayea Paulo Jorge Coelho Ivan Miguel Pires Department of Computer Science University of Management and Technology Sialkot Pakistan Information System Study Program Faculty of Science and Technology Universitas Airlangga School of Software Northwestern Polytechnical University Xian Shaanxi P.R. China Department of Electronics and Communication Engineering Istanbul Technical University(ITU) Turkey Polytechnic University of Leiria Leiria Portugal Institute for Systems Engineering and Computers at Coimbra (INESC Coimbra) Coimbra Portugal Instituto de Telecomunicações Escola Superior de Tecnologia e Gestão de Águeda Universidade de Aveiro Águeda Portugal

Alzheimer's disease (AD) is a type of dementia that leads to memory loss and impairment, which afects patients’ lives badly. It is not curable yet, but its progression can be slowed down if detected at earlier stages. In this research study, we propose a transfer learning-based convolutional neural network (CNN) model to classify magnetic resonance imaging (MRI) into one of four stages of Alzheimer's disease. One of the major limitations of the deep learning-based classification model is the non-availability of healthcare datasets related to AD. The widely used Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset has a major class imbalance issue. We propose a generative adversarial network (GAN) based data augmentation technique to overcome the data imbalance. This promotes the investigation of applying GANs to generate synthetic samples for minority classes in Alzheimer's disease datasets to enhance classification performance. The results show the progression in the overall classification process of AD.

关键词： Alzheimer disease (AD) Deep learning Convolutional Neural Network (CNN) computer-aided diagnosis (CAD)

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Low-Temperature Detection of Acetone Gas Using Zno-Tio2 Nanorod on Tapered Multimode Fiber Decorated with Au-Pd Thin Films

SSRN

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SSRN 2023年

作者： Saidin, Norazlina Roslan, Farah Sakiinah Thirunavakkarasu, Punithavathi M. Khan, Abdul Ali Yaacob, Mohd Hanif Hasbullah, Nurul Fadzlin Department of Electrical and Computer Engineering International Islamic University Malaysia Kuala Lumpur53100 Malaysia Communication Technology Section Universiti Kuala Lumpur British Malaysian Institute Bt. 8 Jalan Sungai Pusu Selangor Gombak53100 Malaysia Department of Telecommunication Engineering Balochistan University of Information Technology Engineering and Management Sciences Quetta87300 Pakistan Photonics System Engineering Laboratory of University Putra Malaysia Selangor Seri Kembangan43300 Malaysia

This paper presents the influence of titanium dioxide (TiO2) as a catalyst to the zinc oxide (ZnO) nanorods coated on the tapered MMF fiber as an acetone gas sensing for lowtemperature detection. The ZnO-TiO2 nanorod was decorated with Au-Pd thin films to enhance the sensitivity. Hydrothermal method was used to synthesize the ZnO nanorods while TiO2 powder is incorporated by using simple doping procedure during fabrication. The decoration of the Au-Pd thin films is integrated onto the ZnO-TiO2 by using DC sputtering method. The ZnO doped with 15 mM TiO2 showed a stronger and significant peak based on XRD analysis which can be indexed to the hexagonal phase of ZnO. A few globular structures of TiO2 were randomly distributed as observed by the SEM image. Four different fiber sensing layers were prepared with respect to 10 mM ZnO precursor;ZnO nanorods (ZN), ZnO nanorods decorated with Au-Pd thin films (ZNAP), ZnO-15mM TiO2 nanorods (ZTN) and ZnO-15mM TiO2 nanorods decorated with Au-Pd thin films (ZTNAP). The spectrometry setup was used to evaluate the light intensity for acetone sensing response in relation to its wavelength. Notable output intensity responses were observed for ZTNAP in terms of sensitivity and standard deviation towards various acetone concentrations while the repeatability curve showed a high response of acetone gas at 10,000 ppm. All experiments were performed at the low temperature of 87 °C. © 2023, The Authors. All rights reserved.

关键词： Titanium dioxide

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Towards silicon photonic neural networks for artificial intelligence

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Science China(Information Sciences) 2020年第6期63卷 105-118页

作者： Bowen BAI Haowen SHU Xingjun WANG Weiwen ZOU State Key Laboratory of Advanced Optical Communications System and Networks School of Electronics Engineering and Computer Science Peking University Frontiers Science Center for Nano-optoelectronics Peking University State Key Laboratory of Advanced Optical Communication Systems and Networks Intelligent Microwave Lightwave Integration Innovation Center(iMLic) Department of Electronic Engineering Shanghai Jiao Tong University

Brain-inspired photonic neural networks for artificial intelligence have attracted renewed interest. For many computational tasks, such as image recognition, speech processing and deep learning, photonic neural networks have the potential to increase the computing speed and energy efficiency on the orders of magnitude compared with digital electronics. Silicon Photonics, which combines the advantages of electronics and photonics, brings hope for the large-scale photonic neural network integration. This paper walks through the basic concept of artificial neural networks and focuses on the key devices which construct the silicon photonic neuromorphic systems. We review some recent important progress in silicon photonic neural networks, which include multilayer artificial neural networks and brain-like neuromorphic systems, for artificial intelligence. A prototype of silicon photonic artificial intelligence processor for ultra-fast neural network computing is also proposed. We hope this paper gives a detailed overview and a deeper understanding of this emerging field.

关键词： photonic neural networks artificial intelligence deep learning photonic computing accelerator silicon photonics

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A Survey on Indoor Visible Light Positioning systems: Fundamentals, Applications, and Challenges

arXiv

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arXiv 2024年

作者： Zhu, Zhiyu Yang, Yang Chen, Mingzhe Guo, Caili Cheng, Julian Cui, Shuguang The Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China The Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States The Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China The Faculty of Applied Science School of Engineering The University of British Columbia KelownaBCV1V 1V7 Canada The Chinese University of Hong Kong Shenzhen518172 China

The growing demand for location-based services in areas like virtual reality, robot control, and navigation has intensified the focus on indoor localization. Visible light positioning (VLP), leveraging visible light communications (VLC), becomes a promising indoor positioning technology due to its high accuracy and low cost. This paper provides a comprehensive survey of VLP systems. In particular, since VLC lays the foundation for VLP, we first present a detailed overview of the principles of VLC. Then, we provide an in-depth overview of VLP algorithms. The performance of each positioning algorithm is also compared in terms of various metrics such as accuracy, coverage, and orientation limitation. Beyond the physical layer studies, the network design for a VLP system is also investigated, including multi-access technologies, resource allocation, and light-emitting diode (LED) placements. Next, the applications of the VLP systems are overviewed. Finally, this paper outlines open issues, challenges, and opportunities for the research field. In a nutshell, this paper constitutes the first holistic survey on VLP from state-of-the-art studies to practical uses. Copyright © 2024, The Authors. All rights reserved.

关键词： Resource allocation

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Coalition Formation for Heterogeneous Federated Learning Enabled Channel Estimation in RIS-assisted Cell-free MIMO

arXiv

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arXiv 2025年

作者： Qi, Nan Liu, Haoxuan Tsiftsis, Theodoros A. Boulogeorgos, Alexandros-Apostolos A. Zhou, Fuhui Jin, Shi Wu, Qihui Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Ministry of Industry and Information Technology Nanjing University of Aeronautics and Astronautics Nanjing210016 China Department of Informatics and Telecommunications University of Thessaly Lamia35100 Greece Department of Electrical and Computer Engineering University of Western Macedonia Kozani50100 Greece National Mobile Communications Research Laboratory Frontiers Science Center for Mobile Information Communication and Security Southeast University Nanjing210096 China

Downlink channel estimation remains a significant bottleneck in reconfigurable intelligent surface-assisted cell-free multiple-input multiple-output communication systems. Conventional approaches primarily rely on centralized deep learning methods to estimate the high-dimensional and complex cascaded channels. These methods require data aggregation from all users for centralized model training, leading to excessive communication overhead and significant data privacy concerns. Additionally, the large size of local learning models imposes heavy computational demands on end users, necessitating strong computational capabilities that most commercial devices lack. To address the aforementioned challenges, a coalition-formation-guided heterogeneous federated learning (FL) framework is proposed. This framework leverages coalition formation to guide the formation of heterogeneous FL user groups for efficient channel estimation. Specifically, by utilizing a distributed deep reinforcement learning (DRL) approach, each FL user intelligently and independently decides whether to join or leave a coalition, aiming at improving channel estimation accuracy, while reducing local model size and computational costs for end users. Moreover, to accelerate the DRL-FL convergence process and reduce computational burdens on end users, a transfer learning method is introduced. This method incorporates both received reference signal power and distance similarity metrics, by considering that nodes with similar distances to the base station and comparable received signal power have a strong likelihood of experiencing similar channel fading. Massive experiments performed that reveal that, compared with the benchmarks, the proposed framework significantly reduces the computational overhead of end users by 16%, improves data privacy, and improves channel estimation accuracy by 20%. © 2025, CC BY.

关键词： Federated learning

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A Joint Transmit and Receive Design for Dimmable High Speed MC MU VLC systems

A Joint Transmit and Receive Design for Dimmable High Speed ...

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GLOBECOM 2022 - 2022 IEEE Global communications Conference

作者： Yujie Yang Yang Yang Hailun Xia Caili Guo Mahdi Chehimi Walid Saad Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing University of. Posts and Telecommunications Beijing China Bradley Department of Electrical and Computer Engineering Wireless@VT Group Virginia Tech Blacksburg VA USA

ISBN: (纸本)9781665435413

In multi-cell multi-user multiple-input multiple-output (MC MU-MIMO) visible light communication (VLC) systems, each user is equipped with multiple closely-placed photodiodes (PDs) with similar channel gains, leading to severe inter-cell interference and intra-cell interference. To address this problem, this paper proposes a hybrid dimming (HD) scheme with MIMO VLC transceiver design, which jointly optimizes transmit and receive antenna selection (TRAS), cell clustering and precoding (TRASP-HD) for MC MU-MIMO VLC systems. In this scheme, a sum-rate maximization problem under the dimming level and illumination uniformity is formulated and solved by being divided into two sub-problems. In particular, The first sub-problem is on TRAS and cell formation based on the criterion of sum-rate maximization under the illumination uniformity constraint. With the same goal, the second sub-problem is on optimizing the precoding matrices of each cell. Finally, these two sub-problems are iteratively solved to obtain a convergent solution. Simulation results verify that in a typical indoor scenario, the mean bandwidth efficiency of TRASP-HD scheme is 2.36 bit/s/Hz higher than the conventional MC MU-MIMO system.

关键词： Spectral efficiency Precoding Simulation Lighting Receiving antennas Transceivers MIMO

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A Tutorial on Extremely Large-Scale MIMO for 6G: Fundamentals, Signal Processing, and Applications

arXiv

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arXiv 2023年

作者： Wang, Zhe Zhang, Jiayi Du, Hongyang Niyato, Dusit Cui, Shuguang Ai, Bo Debbah, Mérouane Letaief, Khaled B. Poor, H. Vincent The School of Electronic and Information Engineering Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong University Beijing100044 China The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore Shenzhen518172 China The Department of Broadband Communication Peng Cheng Laboratory Shenzhen518000 China The Center for 6G Technology The Khalifa University of Science and Technology Abu Dhabi United Arab Emirates Hong Kong The Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

Extremely large-scale multiple-input-multiple-output (XL-MIMO), which offers vast spatial degrees of freedom, has emerged as a potentially pivotal enabling technology for the sixth generation (6G) of wireless mobile networks. With its growing significance, both opportunities and challenges are concurrently manifesting. This paper presents a comprehensive survey of research on XL-MIMO wireless systems. In particular, we introduce four XL-MIMO hardware architectures: uniform linear array (ULA)-based XL-MIMO, uniform planar array (UPA)-based XL-MIMO utilizing either patch antennas or point antennas, and continuous aperture (CAP)-based XL-MIMO. We comprehensively analyze and discuss their characteristics and interrelationships. Following this, we introduce several electromagnetic characteristics and general distance boundaries in XL-MIMO. Given the distinct electromagnetic properties of near-field communications, we present a range of channel models to demonstrate the benefits of XL-MIMO. We further discuss and summarize signal processing schemes for XL-MIMO. It is worth noting that the low-complexity signal processing schemes and deep learning empowered signal processing schemes are reviewed and highlighted to promote the practical implementation of XL-MIMO. Furthermore, we explore the interplay between XL-MIMO and other emergent 6G technologies. Finally, we outline several compelling research directions for future XL-MIMO wireless communication systems. Copyright © 2023, The Authors. All rights reserved.

关键词： MIMO systems

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