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Congestion Relief in Downlink OFDMA Cellular System Using Inter-Cell Relay

China Communications 2019年第4期16卷 35-46页

作者： Weifeng Lu Mingqi Zhu Jia Xu Siguang Chen Lijun Yang Jian Xu School of Computer Science Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications College of Internet of Things Nanjing University of Posts and Telecommunications School of Computer Science and Engineering Nanjing University of Science and Technology

This paper proposes the concept of inter-cell relay for downlink orthogonal frequency division multiple access(OFDMA) cellular systems, which uses multi-hop to relay calls from overloaded cells to light-load neighboring cells. It is shown that when using inter-cell relay, the number of calls in the congestion cell can be significantly increased. The congestion cell is divided into two parts. One is called non-relay area(NRA), in which a call directly communicates with the base station(BS) of a congested cell. The other is called relay area(RA), in which a call communicates with the BS of a neighboring cell through a relay station(RS). The two parts have different user-call densities. By adjusting the densities of two parts, we will maximize the number of supported calls inside a congested cell. The results show the benefits gained from inter-cell relay in congestion relief, which can reduce cell congestion by fully utilizing the available resources in the neighboring cells.

关键词： OFDMA cellular system congestion relief optimization problem inter-cell relay

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DB-SBR: A dual-backbone model for student behavior recognition

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Pattern Recognition 2026年 169卷

作者： Lin Wang Xiaolong Xu Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications No. 9 Wenyuan Road Nanjing 210023 PR China School of Computer Science Nanjing University of Posts and Telecommunications No. 9 Wenyuan Road Nanjing 210023 PR China

Aiming at the challenge of student behavior recognition in such complex scenarios as smart classroom, we propose a Dual-Backbone Model for Student Behavior Recognition (DB-SBR). First, we innovatively propose a dual-backbone feature extraction scheme for the same input, which effectively improves the robustness and feature extraction capability of the model. Among them, we design the CSP Bottleneck with Partial Convolution (CSPC) module for the construction and optimization of the CSPC ConvNet backbone network, and construct the Simplified Swin Transformer (SST) backbone network through structural optimization, which makes the model achieve a better balance between efficiency and accuracy. In addition, we propose an Adaptive Weighted Attention Feature Fusion (AAFusion) module, which realizes more effective feature integration and further promotes the comprehensive performance of the model. Experimental results on the SCB-dataset3 demonstrate that DB-SBR outperforms existing methods, achieving F1-score, [email protected] , and [email protected] :0.95 of 0.687, 0.729, and 0.544, respectively, with improvements of 2.5 %, 3.7 %, and 4.2 % over the baseline. DB-SBR provides a new solution for high-quality student behavior recognition in smart classrooms.

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A new classification method of SAR image based on OGMRF-RC model 5

A new classification method of SAR image based on OGMRF-RC m...

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IET International Radar Conference 2020, IET IRC 2020

作者： Li, Yuan Guo, Zhishun Wu, Lin Guo, Zhengwei College of Computer and Information Engineering Henan University Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China Henan Engineering Research Center of Intelligent Technology and Application Henan University Kaifeng475000 China College of Environment and Planning Henan University Kaifeng475004 China

ISBN: (纸本)9781839535635

Synthetic Aperture Radar (SAR) image classification is a key technique and research focus in the application of remote sensing. This paper proposes a new classification method of SAR image based on Object-based Gaussian-Markov Random Field model with Region Coefficients (OGMRF-RC), the new method use Fuzzy Probability (FP) represents the region's category probability, FP is obtained by regional posterior probability and edge information of neighborhood. Use FP to replace the unique class label of the region, which avoids the parameters estimation inaccurate of the feature field caused by the unique initial class label. And the parameter calculation method of feature field takes into account the feature information of all regions in the image. In this paper, we take the area around Baliwan in Kaifeng as study area, Sentinel-1 is used as the remote sensing data sources. FCM, K-means, classic MRF and OGMRF-RC are used as contrast methods, the experimental result demonstrates the accuracy of our method is better than these methods. © 2020 Institution of Engineering and Technology. All rights reserved.

关键词： Synthetic aperture radar

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Temperature prediction modeling and control parameter optimization based on data driven 5

Temperature prediction modeling and control parameter optimi...

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5th IEEE International Conference on data Science in Cyberspace, DSC 2020

作者： Liu, Qingguang Wei, Jielong Lei, Sining Huang, Qingbao Zhang, Mengqiao Zhou, Xiongbin Guangxi University School of Electrical Engineering Nanning China School of EE Guangxi University China School of Software Engineering South China University of Technology China Key Laboratory of Big Data and Intelligent Robot Ministry of Education China Alnan Aluminium Lnc. Alnan Aluminium Co. Ltd Nanning China

ISBN: (数字)9781728195582

ISBN: (纸本)9781728195582

The aluminum smelting process is characterized with multi-variable, strong coupling and large lag, which leads to low efficiency and high energy consumption. Previous study shows that Extreme learning machine (ELM) can be used to model furnace temperature for its high train speed. However, due to the random initialization of the input weight and the hidden layer threshold in the ELM, the furnace temperature prediction model is unstable in the start of each smelting process. To improve the stability, we introduce the restricted Boltzmann machine to optimize an ELM model. In addition, a furnace temperature segmentation optimization strategy based on whale optimization algorithm is proposed to effectively find the optimal values of the control parameters in each furnace temperature section. The simulation results state that the aluminum melting time can be reduced by about 6.67% after optimization. © 2020 IEEE.

关键词： Optimization

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Graph convolutional network for visual question answering based on fine-grained question representation 5

Graph convolutional network for visual question answering ba...

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5th IEEE International Conference on data Science in Cyberspace, DSC 2020

作者： Hu, Ze Wei, Jielong Huang, Qingbao Liang, Hanyu Zhang, Xingmao Liu, Qingguang Guangxi University School of Electrical Engineering Nanning Guangxi China School of Software Engineering South China University of Technology Guangzhou Guangdong China Key Laboratory of Big Data and Intelligent Robot South China University of Technology Ministry of Education China Guangxi Arts University General Education Department Nanning Guangxi China

ISBN: (数字)9781728195582

ISBN: (纸本)9781728195582

Visual Question Answering (VQA) requires a fine-grained and simultaneous understanding of both the visual content of images and the textual content of questions. Recent research works mainly focus on the bilinear fusion methods and attention mechanism methods to explore the global features and local features, respectively. When an image exists multiple objects, the fine-grained question features can effectively identify the relationship between objects in an image. Therefore we propose a graph convolutional network based on fine-grained question representation (FQ-GCN). The object relation graph is firstly constructed. The fine-grained question features are used to explore the relations between objects in an image and prune unrelated edges between object nodes. Graph convolutional network is used to aggregate all neighborhood information of each object in an object graph. Experiments on the VQA 2.0 dataset show that the performance of our FQ-GCN model improves 2% ~ 3% than the classical methods. © 2020 IEEE.

关键词： Convolution

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data-driven Industrial Robot Arm Calibration: A Machine Learning Perspective

Data-driven Industrial Robot Arm Calibration: A Machine Lear...

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IEEE International Conference on Networking, Sensing and Control

作者： Zhibin Li Shuai Li Xin Luo School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing School University of Chinese Academy of Sciences Chongqing China Swansea University Swansea UK Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing School University of Chinese Academy of Sciences Chongqing China

ISBN: (纸本)9781665440493

Robot arms have been widely used in industry. The absolute positioning error of robots without calibration can reach several millimeters, which cannot meet the application requirements of accurate operation. Therefore, it is almost a mandatory procedure for industrial robots to take on-site calibration before being used. Generally, most researchers on robot calibration have mechanical and instrumentation background as the collection of calibration data is tedious and it is usually difficult to access to industrial robots for researchers in other fields. This research explores the calibration problem from a machine learning perspective and provides the first open-access dataset called "RobotCali" in this area so that machine learning scientists can step into this field and verify their algorithms on this problem. In the meanwhile, a new calibration method based on the Levenberg-Marquardt (LM) algorithm and extended Kalman filter (EKF) algorithm is proposed, which can significantly improve the absolute positioning accuracy of the robot after calibration. Firstly, the error model of robot is established, and kinematic parameters are initially identified by LM algorithm. Then the EKF algorithm is used to further calibrate these parameters, which has been verified the effectiveness of the proposed method by experimental results. Lastly, the future research work is discussed.

关键词： Machine learning algorithms Service robots Machine learning Robot sensing systems Manipulators Calibration Sensors

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Improved small gain conditions for input-to-state stability with respect to measurement functions: Discrete time networked system

Improved small gain conditions for input-to-state stability ...

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IEEE Conference on Decision and Control

作者： Yuanqiu Mo Changbin Yu Soura Dasgupta Westlake Institute for Advanced Study Westlake University Hangzhou China College of Artificial Intelligence and Big Data Shandong First Medical University & Shandong Academy of Medical Sciences Jinan China Institute for Intelligent Robots Fudan University Shanghai China Shandong Computer Science Center Shandong Provincial Key Laboratory of Computer Networks China Faculty of Engineering & the Built Environment University of Johannes-Burg Johannesburg South Africa University of Iowa Iowa City Iowa USA

ISBN: (纸本)9781665436601

In this paper we study input-to-state stability with respect to measurement functions for discrete time networked systems. In such a networked system, the trajectory of each subsystem is affected by another in each time step, and this constraining system may change from step to step. We derive small gain conditions of input-to-state stability with respect to measurement functions for this type of discrete time networked system relying on the construction of dissipative-form finite time Lyapunov function. These conditions loosen those presented in [1] and [2]. Finally, we demonstrate the applicability of our results by applying them to the stability analysis of a distributed graph algorithm which does not satisfy the conditions of [1] and [2].

关键词： Network topology Conferences Gain measurement Stability analysis Time measurement Trajectory Topology

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SLAs-aware online task scheduling based on deep reinforcement learning method in cloud environment 21

SLAs-aware online task scheduling based on deep reinforcemen...

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21st IEEE International Conference on High Performance Computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Ran, Longyu Shi, Xiaoyu Shang, Mingsheng Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing400714 China University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9781728120584

As increasingly traditional applications migrate to the cloud, load balancing without sacrificing the performances of cloud-based applications is a highly challenging problem in cloud computing. Existing solutions focusing on online task scheduling have the inefficient issue, due to the highly dynamic nature of cloud workloads and the virtual machines (VMs) with heterogeneous hardware configurations. To address this issue, dependent on the strong perception and decision-making ability of deep reinforcement learning (DRL) in automatic control problems, we propose a deep reinforcement learning based task scheduling method, which can dynamic assign submitted tasks to different VMs. In details, we formulate the task scheduling as a dynamical optimal problem with constraints, and then adopt the deep deterministic policy gradients (DDPG) network to find the optimal task assignment solution with meeting the Service Level Agreements (SLAs) requirements. It makes the optimal scheduling decision only dependent on learning directly from its experience without any prior knowledge. The experimental results using the actual Alibaba cloud workload tracings show that compared with other existing solutions, our proposed task scheduling method can effectively reduce the average response time of tasks with guaranteeing the load balancing among VMs, when facing of dynamical workloads. © 2019 IEEE.

关键词： Reinforcement learning

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Insar Time-Series Deformation Forecasting Surrounding Salt Lake Using Deep Transformer Models

SSRN

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SSRN 2022年

作者： Wang, Jing Li, Chao Li, Lu Huang, Zhihua Wang, Chao Zhang, Hong Zhang, Zhengjia Research Institute of Intelligent Computing Zhejiang Laboratory. Hangzhou311121 China Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences No.9 Dengzhuang South Road Beijing100094 China International Research Center of Big Data for Sustainable Development Goals Beijing100094 China University of Chinese Academy of Sciences Beijing100049 China Faculty of Information Engineering China University of Geosciences. 388 Lumo Road Wuhan430074 China

The open source strategy of Sentinel-1 SAR images enables Radar interferometry (InSAR) to perform time series surface deformation monitoring over large *** deformation monitoring and prediction can investigate the freeze-thaw cycles of permafrost on the Qinghai-Tibet Plateau (QTP). However, the convolutional and recurrent neural networks cannot accurately model long-term and complex relations in multi-variate time series data, it is challenging to implement time series deformation prediction with high spatial resolution. We propose an InSAR deformation prediction integrated algorithm based on the transformer-based models to predict time series deformation more accurately in permafrost areas. This method takes advantage of the self-attention mechanism to study complicated dynamic deformation features of permafrost caused by temperature and other variables from InSAR time series ***, Sentinel-1 dataset acquired with 12-day repeat are collected to implement the Small Baseline Subset (SBAS) method and retrieve the seasonal and long-term deformation surrounding Salt Lake. Then, the InSAR time series deformation and meteorological data are combined to establish the deformation prediction model using the transformer neural network. These results demonstrated that the InSAR deformation prediction method achieves better prediction performance (the root mean square errors are 3.207mm and 2.27mm) in predicting permafrost deformation trends with a point scale compared with the prediction results of other models. Finally, the predicted time series deformation maps for the period 2021/04/05 to 2022/03/19 are mapped surrounding Salt Lake. Meanwhile the total area of Salt Lake increased by 57.32km 2 during the period 2015-2019 based on the predicted deformation and the water extraction *** the expansion rate of Salt Lake was slow, and the area decreased by 3.26 km 2 from 2019 to 2022. The time series deformation forecasting method provides early warn

关键词： Deformation

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CASIA-Face-Africa: A large-scale African face image database

arXiv

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arXiv 2021年

作者： Muhammad, Jawad Wang, Yunlong Wang, Caiyong Zhang, Kunbo Sun, Zhenan The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China The Center for Research on Intelligent Perception and Computing National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China The School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing100044 China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing100044 China

Face recognition is a popular and well-studied area with wide applications in our society. However, racial bias had been proven to be inherent in most State Of The Art (SOTA) face recognition systems. Many investigative studies on face recognition algorithms have reported higher false positive rates of African subjects cohorts than the other cohorts. Lack of large-scale African face image databases in public domain is one of the main restrictions in studying the racial bias problem of face recognition. To this end, we collect a face image database namely CASIA-Face-Africa which contains 38,546 images of 1,183 African subjects. Multi-spectral cameras are utilized to capture the face images under various illumination settings. Demographic attributes and facial expressions of the subjects are also carefully recorded. For landmark detection, each face image in the database is manually labeled with 68 facial keypoints. A group of evaluation protocols are constructed according to different applications, tasks, partitions and scenarios. The performances of SOTA face recognition algorithms without retraining are reported as baselines. The proposed database along with its face landmark annotations, evaluation protocols and preliminary results form a good benchmark to study the essential aspects of face biometrics for African subjects, especially face image preprocessing, face feature analysis and matching, facial expression recognition, sex/age estimation, ethnic classification, face image generation, etc. The database can be downloaded from our website* Copyright © 2021, The Authors. All rights reserved.

关键词： Face recognition

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