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Resource Allocation for D2D Communication Underlaying Cellular Networks: A Distance-Based Grouping Strategy

Wireless Communications and Mobile Computing 2023年第1期2023卷

作者： Gao, Jing Meng, Xiao Yang, Chen Zhang, Bo Yi, Xin School of Control Engineering Northeastern University at Qinhuangdao Qinhuangdao066004 China School of Computer Science and Engineering Northeastern University Shenyang110819 China

Device-to-device (D2D) communication with direct terminal connection is a promising candidate for 5G communication, which increases the capacity of cellular networks and spectral efficiency. Introducing D2D communication to cellular users (CUs) will increase system capacity, and CUs will provide reusable channel resources for D2D users (DUs). However, the sharing of channel resources between CUs and DUs will lead to cofrequency interference and affect the communication quality of user terminals. As a means of improving spectrum utilization and solving cofrequency interference problems, a one-to-many D2D communication system model is established in cellular networks. Through model analysis, the interference between CUs and DUs is correlated with their distance from one another. Considering the different interference of CUs to DUs at different distances, an algorithm for resource allocation based on distance grouping is proposed. With this algorithm, DUs will reuse channel resources of CUs within a reasonable distance in the group, and interference between DUs and CUs will be minimized. The improved particle swarm optimization algorithm is used to solve the optimal power, to achieve the maximum transmission rate of the system. Simulated results show that the algorithm will significantly improve system throughput and performance while also lowering the computational complexity of the algorithm, enabling the whole system to have better communication quality. Copyright © 2023 Jing Gao et al.

关键词： Resource allocation

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Retrospect and prospect on integration of gain and phase for feedback stability analysis

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control Theory and Technology 2022年第4期20卷 555-558页

作者： Di Zhao Li Qiu Department of Control Science and Engineering&Shanghai Institute of Intelligent Science and Technology Tongji UniversityShanghai 201804China Department of Electronic and Computer Engineering The Hong Kong University of Science and TechnologyClear Water BayKowloonHong KongChina Guangzhou HKUST Fok Ying Tung Research Insititute Nansha DistrictGuangzhou 511458GuangdongChina

In this paper,we review existing approaches to integrating small gain and small phase analysis for feedback stability of dynamical systems,and give a brief outlook for possible future directions in exploring this *** gain analysis has been very successful and popular in control theory since 1960s,while the small phase analysis for multiple-input-multiple-output systems has not been well understood until recently and is now gradually taking ***,there have been attempts to analyzing feedback stability via the integration of gain and phase information over decades,including the combination of small gain with positive realness as well as that with negative *** combinations can be subsumed into a recently proposed framework for gain-phase integration,which brings in new geometrical methods and also sheds new lights on several future directions.

关键词： Small gain Small phase Passivity Positive real Negative imaginary Davis-Wielandt shell

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Federated Multi-View Multi-Label Classification

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IEEE Transactions on Big Data 2024年

作者： Meng, Hongdao Deng, Yongjian Zhong, Qiyu Wang, Yipeng Yang, Zhen Lyu, Gengyu Beijing University of Technology College of Computer Science China Ministry of Education Engineering Research Center of Intelligence Perception and Autonomous Control Beijing100124 China

Multi-view multi-label classification is a crucial machine learning paradigm aimed at building robust multi-label predictors by integrating heterogeneous features from various sources while addressing multiple correlated labels. However, in real-world applications, concerns over data confidentiality and security often prevent data exchange or fusion across different sources, leading to the challenging issue of data islands. To tackle this problem, we propose a general federated multi-view multi-label classification method, FMVML, which integrates a novel multi-view multi-label classification technique into a federated learning framework. This approach enables cross-view feature fusion and multi-label semantic classification while preserving the data privacy of each independent source. Within this federated framework, we first extract view-specific information from each individual client to capture unique characteristics and then consolidate consensus information from different views on the global server to represent shared features. Unlike previous methods, our approach enhances cross-view fusion and semantic expression by jointly capturing both feature and semantic aspects of specificity and commonality. The final label predictions are generated by combining the view-specific predictions from individual clients and the consensus predictions from the global server. Extensive experiments across various applications demonstrate that FMVML fully leverages multi-view data in a privacy-preserving manner and consistently outperforms state-of-the-art methods. © 2015 IEEE.

关键词： Federated learning

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Application of Machine Learning Models for Predicting Students' Performance in Mathematics: A K-Fold Approach

Application of Machine Learning Models for Predicting Studen...

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2024 International Conference on science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Ale, Felix Ikpaya, Ikpaya D. Danyan, Ilesanmi Opateye, Goodness O. Onwodi, Gregory Agboola, Olufemi O. National Space Research & Development Agency Department of Engineering & Space Systems Abuja Nigeria National Space Research & Development Agency Ground Station & Mission Control Abuja Nigeria Achievers University Department of Mechanical & Mechatronics Engineering Owo Nigeria National Open University of Nigeria Department of Computer Science Abuja Nigeria

ISBN: (纸本)9798350358155

Mathematics plays a pivotal role in science, technology and engineering. It is one of the compulsory subjects to gain admission into a higher institution in Nigeria. Considering its importance, students' performance has been a significant concern for most educators, government, parents and guardians. This study aims to predict students' performance in mathematics using the K-fold approach to support effective decision-making and policy implementation on mathematics. The primary dataset was collected from 192 senior secondary schools, and one mathematics student was randomly selected from five Senior Secondary Schools in Bwari Area Council, Abuja, and used as a sample. Students' Mathematics Achievement Test (SMAT) was used as the instrument to collect data. The data was processed and modelled using the Python Programming language. Thus, five machine learning models were trained and evaluated to overcome overfitting using four-, five - -, and six-fold K-Fold cross-validation techniques. Five folds performed best for all the models except for the Support Vector Regressor, which performed best with six folds. The evaluation of the models showed that the Light Gradient Boosting Model outperformed others with a Mean Absolute Error of 12.642 and a Root Mean Square Error of 15.894. The models were significantly influenced by the students' personal study time, friends' assistance, and the parents' educational background. It is therefore recommended that academic researchers, educational authorities in Nigeria, and relevant stakeholders use and improve on this model to predict the students' scores and assist them in achieving great performance in mathematics. © 2024 IEEE.

关键词： Mean square error

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Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Learning 22

Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Lea...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Cao, Weipeng Yao, Xuyang Xu, Zhiwu Pan, Yinghui Sun, Yixuan Li, Dachuan Qiu, Bohua Wei, Muheng Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Stony Brook University New York United States Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China ZhenDui Industry Artificial Intelligence Co. Ltd Shenzhen China Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (纸本)9798331527471

Realizing Generalized Zero-Shot Learning (GZSL) based on large models is emerging as a prevailing trend. However, most existing methods merely regard large models as black boxes, solely leveraging the features output by the final layer while disregarding potential performance enhancements from other layers. Indeed, numerous researchers have visually depicted variations in the features learned across different layers of neural networks. Motivated by this observation, we propose a Vision Transformer (ViT)-based GZSL method named Depth-Aware Multi-Modal ViT (DAM2ViT), which exploits multi-level features of ViT. DAM2ViT incorporates a multi-modal interaction block to align semantic information of categories across multiple layers, thereby augmenting the model’s capacity to learn associations between visual and semantic spaces. Extensive experiments conducted on three benchmark datasets (i.e., CUB, SUN, AWA2) have showcased that DAM2ViT achieves competitive results compared to state-of-the-art methods. © 2024 IEEE.

关键词： Zero-shot learning

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Business Process Representation Based on Graph Convolutional Network 1st

Business Process Representation Based on Graph Convolutiona...

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1st Conference on Blockchain and Web3.0 Technology Innovation and Application, BWTAC 2024

作者： Zeng, Qingtian Zhang, Lin Cao, Rui Guo, Wenyan Li, Chao College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao Shandong China School of Information and Control Engineering Qingdao University of Technology Qingdao China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China

ISBN: (纸本)9789819794119

The effective representation of business processes is a key problem of predictive monitoring based on deep learning networks. Most of the existing business process representation methods are unable to accurately reflect the characteristics and node information aggregation of events in the graph structure. Therefore, a business process representation method based on graph convolutional network is proposed. Firstly, the diagram structure is constructed based on the same prefix window event of the business process. Secondly, on the basis of the constructed graph structure data, the node vectorization representation based on graph convolutional network model is given, and the information is aggregated through the graph convolutional network. Then, the depth representation of nodes is applied to the downstream prediction task of the business process. Finally, five real datasets are used for experimental evaluation. The experimental results show that the proposed event log graph construction strategy and process representation method are effective in business process prediction. The source code of this work is available at https://***/LinZhang0/GCNp. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Graph neural networks

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An Efficient Particle YOLO Detector for Urine Sediment Detection 4th

An Efficient Particle YOLO Detector for Urine Sediment Dete...

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4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Chen, Zejian Hu, Rong Chen, Fukun Fan, Haoyi Ching, Fum Yew Li, Zuoyong Su, Shimei Fujian Provincial Key Laboratory of Big Data Mining and Applications School of Computer Science and Mathematics Fujian University of Technology Fujian Fuzhou350118 China School of Computer and Artificial Intelligence Zhengzhou University Henan Zhengzhou450001 China School of Computer Sciences Universiti Sains Malaysia Penang11800 Malaysia Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fujian Fuzhou350121 China School of Electrical Engineering Zhengzhou University Henan Zhengzhou450001 China

ISBN: (纸本)9783031201011

Urine sediment detection is an essential aid in assessing kidney health. Traditional machine learning approaches treat urine sediment particle detection as an image classification task, segmenting particles for detection based on information such as edges or thresholds. However, the segmentation of sediment particles is complex due to the low contrast and weak edge characteristics of urine sediment images. In this paper, we consider urine sediment particle detection as a object detection task and propose the YOLOv5s-CBL, a detector dedicated to particle detection. Specifically, to mitigate the impact of background noise on detection accuracy, we inherit CBAM on the YOLOv5s model to help the network filter useless noise information and find regions of interest to extract target features. Then, we expand the original three-scale feature layer to improve the sensitivity of the model to larger-scale target sediment particles. Finally, we use the BiFPN structure instead of the original PANet combined with the FPN structure, which can more effectively fuse multiple different scales of information to improve the detection performance of the model. We compared state-of-the-art methods on two real-world datasets, and the experimental results demonstrated the effectiveness of YOLOv5s-CBL. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Object detection

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Semi-Markov Telecommunication Networks Model

Semi-Markov Telecommunication Networks Model

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2024 International Conference on Actual Problems of Electron Devices engineering, APEDE 2024

作者： Svetlov, Mikhail S. L’vov, Alexey A. Martirosyan, Arman M. Martirosyan, David M. Bagaev, Igor S. Khasanov, Semen T. Laboratory of Control and Automation Problems in Mechanical Engineering Institute of Precision Mechanics and Control of RAS Saratov Russia Institute of Institute of Electronic Engineering and Instrumentation Yuri Gagarin State Technical University of Saratov Saratov Russia Institute of Applied Information Technologies and Communications Yuri Gagarin State Technical University of Saratov Saratov Russia Phystech-School of Applied Mathematics and Computer Science Moscow Institute of Physics and Technology Moscow Russia

ISBN: (纸本)9798350393316

This paper proposes a semi-Markov model of telecommunication network (TCN). The variant of dynamic traffic adaptive control of queuing system as a special case of TCN is considered. The main purpose of control is to minimize the average cost per unit of time to service the incoming flow of information (packets). This takes into account the different bandwidth of the channels, the processing speed of information in the channel and the information capacity of the buffers. The approach to the organization of dynamic control taking into account noise immunity (information reliability) and information security is discussed. © 2024 IEEE.

关键词： Bandwidth

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A Physical-Data Fusion Framework for Lithium-ion Battery SOC Estimation*

A Physical-Data Fusion Framework for Lithium-ion Battery SOC...

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Annual Conference of Industrial Electronics Society

作者： Bingzhe Fu Yihuan Li Kailong Liu Wei Lin School of Control and Computer Engineering North China Electric Power University Beijing China School of Control Science and Engineering Shandong University Shandong China

Lithium-ion batteries have increasingly become a primary energy source in Electric Vehicles (EVs), power grid energy storage, aerospace, and other fields. Accurate State of Charge (SOC) estimation is crucial for the safe and efficient operation of lithium batteries. This paper proposes a physical-data fusion framework for accurate SOC estimation of Lithium-ion batteries. First, a fractional-order model (FOM) of lithium-ion batteries is built to describe the electrochemical reactions inside the battery, and a fractional-order Extended Kalman Filter (FOEKF) algorithm is used to achieve preliminary SOC estimation. Second, the FOM is combined with an error model established by Long Short-Term Memory (LSTM) to improve the accuracy of the FOEK-based SOC estimation by compensating for the estimation error. Finally, the proposed framework is verified on Maryland dataset, and the experimental results demonstrate that the fusion framework exhibits superior performance in improving the accuracy of SOC estimation, with at least 75% and 50% reduction in MSE and max error, respectively.

关键词：

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Texture Analysis of Breast US Images Using Morphological Transforms, Hausdorff Dimension and Bagging Ensemble Method 32

Texture Analysis of Breast US Images Using Morphological Tra...

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32nd Mediterranean Conference on control and Automation, MED 2024

作者： Tabacaru, Gigi Moldovanu, Simona Barbu, Marian 'Dunarea de Jos' University of Galati Faculty of Automation Computers Electrical Engineering and Electronics Department of Automatic Control and Electrical Engineering Romania 'Dunarea de Jos' University of Galati Computer Science and Information Technology Faculty of Automation Computers Electrical Engineering and Electronics Romania Dunarea de Jos University of Galati The Modelling & Simulation Laboratory Galati800008 Romania

ISBN: (纸本)9798350395440

In this work, an attempt is made for the first time to use the measurement pattern generated by morphological transformation quantified by Hausdorff fractal dimension (HFD) and classified with ensemble learning based on bagging. The proposed work uses three morphological transformations for image preprocessing: hit-and-miss transform (HMT), white (WHT), and black top-hat (BHT). The pattern texture of US breast images is described by extracting the HFD from the regions of interest (ROI) after the ultrasound (US) images have been preprocessed. The main objective of this study was achieved by comparatively analyzing the classification performance of features using the Random Forest (RF), Extra Trees (ET) classifier, and bagging ensemble method based on XGBoot classifier. In presented study, the XGBoost classifier and BHT image processing method give an accuracy of 89.8% in a binary classification, benign versus malignant breast cancer. © 2024 IEEE.

关键词： Fractal dimension

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