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Energy-optimal DNN model placement in UAV-enabled edge computing networks

Digital Communications and Networks 2024年第4期10卷 827-836页

作者： Jianhang Tang Guoquan Wu Mohammad Mussadiq Jalalzai Lin Wang Bing Zhang Yi Zhou State Key Laboratory of Public Big Data Guizhou UniversityGuiyang550025China School of Information Science and Engineering Yanshan UniversityQinhuangdao066004China School of Engineering The University of British Columbia OkanaganKelownaV1V 1V7Canada College of General Aviation and Flight Nanjing University of Aeronautics and AstronauticsNanjing210016China

Unmanned aerial vehicle(UAV)-enabled edge computing is emerging as a potential enabler for Artificial Intelligence of Things(AIoT)in the forthcoming sixth-generation(6G)communication *** the use of flexible UAVs,massive sensing data is gathered and processed promptly without considering geographical *** neural networks(DNNs)are becoming a driving force to extract valuable information from sensing ***,the lightweight servers installed on UAVs are not able to meet the extremely high requirements of inference tasks due to the limited battery capacities of *** this work,we investigate a DNN model placement problem for AIoT applications,where the trained DNN models are selected and placed on UAVs to execute inference tasks *** is impractical to obtain future DNN model request profiles and system operation states in UAV-enabled edge *** Lyapunov optimization technique is leveraged for the proposed DNN model placement *** on the observed system overview,an advanced online placement(AOP)algorithm is developed to solve the transformed problem in each time slot,which can reduce DNN model transmission delay and disk I/O energy cost simultaneously while keeping the input data queues ***,extensive simulations are provided to depict the effectiveness of the AOP *** numerical results demonstrate that the AOP algorithm can reduce 18.14%of the model placement cost and 29.89%of the input data queue backlog on average by comparing it with benchmark algorithms.

关键词： UAV-Enabled edge computing DNN model Placement 6G networks Inference tasks

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Application of U-Net and Optimized Clustering in Medical Image Segmentation:A Review

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Computer Modeling in engineering & Sciences 2023年第9期136卷 2173-2219页

作者： Jiaqi Shao Shuwen Chen Jin Zhou Huisheng Zhu Ziyi Wang Mackenzie Brown School of Physics and Information Engineering Jiangsu Second Normal UniversityNanjing211200China State Key Laboratory of Millimeter Waves Southeast UniversityNanjing210096China Jiangsu Province Engineering Research Center of Basic Education Big Data Application Nanjing211200China School of Data Science Perdana UniversitySerdangSelangor43400Malaysia

As a mainstream research direction in the field of image segmentation,medical image segmentation plays a key role in the quantification of lesions,three-dimensional reconstruction,region of interest extraction and so *** with natural images,medical images have a variety of ***,the emphasis of information which is conveyed by images of different modes is quite *** it is time-consuming and inefficient to manually segment medical images only by professional and experienced ***,large quantities of automated medical image segmentation methods have been ***,until now,researchers have not developed a universal method for all types of medical image *** paper reviews the literature on segmentation techniques that have produced major breakthroughs in recent *** the large quantities of medical image segmentation methods,this paper mainly discusses two categories of medical image segmentation *** is the improved strategies based on traditional clustering *** other is the research progress of the improved image segmentation network structure model based on *** power of technology proves that the performance of the deep learning-based method is significantly better than that of the traditional *** paper discussed both advantages and disadvantages of different algorithms and detailed how these methods can be used for the segmentation of lesions or other organs and tissues,as well as possible technical trends for future work.

关键词： Medical image segmentation clustering algorithm U-Net

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Phase prediction for high-entropy alloys using generative adversarial network and active learning based on small datasets

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Science China(Technological Sciences) 2023年第12期66卷 3615-3627页

作者： CHEN Cun ZHOU HengRu LONG WeiMin WANG Gang REN JingLi Henan Academy of Big Data School of Mathematics and StatisticsZhengzhou UniversityZhengzhou 450001China State Key Laboratory of Advanced Brazing Filler Metals and Technology Zhengzhou Research Institute of Mechanical EngineeringZhengzhou 450001China Institute of Materials Shanghai UniversityShanghai 200444China

In this paper,a new machine learning(ML)model combining conditional generative adversarial networks(CGANs)and active learning(AL)is proposed to predict the body-centered cubic(BCC)phase,face-centered cubic(FCC)phase,and BCC+FCC phase of high-entropy alloys(HEAs).Considering the lack of data,CGANs are introduced for data augmentation,and AL can achieve high prediction accuracy under a small sample size owing to its special sample selection ***,we propose an ML framework combining CGAN and AL to predict the phase of *** arithmetic optimization algorithm(AOA)is introduced to improve the artificial neural network(ANN).AOA can overcome the problem of falling into the locally optimal solution for the ANN and reduce the number of training *** AOA-optimized ANN model trained by the AL sample selection strategy achieved high prediction accuracy on the test *** improve the performance and interpretability of the model,domain knowledge is incorporated into the feature ***,considering that the proposed method can alleviate the problem caused by the shortage of experimental data,it can be applied to predictions based on small datasets in other fields.

关键词： high-entropy alloys phase prediction machine learning conditional generative adversarial networks active learning

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An Improved YOLOv5s-Based Smoke Detection System for Outdoor Parking Lots

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Computers, Materials & Continua 2024年第8期80卷 3333-3349页

作者： Ruobing Zuo Xiaohan Huang Xuguo Jiao Zhenyong Zhang State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang550025China School of Information and Control Engineering Qingdao University of TechnologyQingdao266520China State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang UniversityHangzhou310027China Key Laboratory of Computing Power Network and Information Security Ministry of EducationQilu University of Technology(Shandong Academy of Sciences)Jinan250353China Text Computing&Cognitive Intelligence Engineering Research Center of National Education Ministry Guizhou UniversityGuiyang550025China

In the rapidly evolving urban landscape,outdoor parking lots have become an indispensable part of the city’s transportation *** growth of parking lots has raised the likelihood of spontaneous vehicle combus-tion,a significant safety hazard,making smoke detection an essential preventative ***,the complex environment of outdoor parking lots presents additional challenges for smoke detection,which necessitates the development of more advanced and reliable smoke detection *** paper addresses this concern and presents a novel smoke detection technique designed for the demanding environment of outdoor parking ***,we develop a novel dataset to fill the gap,as there is a lack of publicly available *** dataset encompasses a wide range of smoke and fire scenarios,enhanced with data augmentation to ensure robustness against diverse outdoor ***,we utilize an optimized YOLOv5s model,integrated with the Squeeze-and-Excitation Network(SENet)attention mechanism,to significantly improve detection accuracy while maintaining real-time processing ***,this paper implements an outdoor smoke detection system that is capable of accurately localizing and alerting in real time,enhancing the effectiveness and reliability of emergency *** show that the system has a high accuracy in terms of detecting smoke incidents in outdoor scenarios.

关键词： YOLOv5s smoke detection deep learning SENet

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Multi-layer network embedding on scc-based network with motif

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Digital Communications and Networks 2024年第3期10卷 546-556页

作者： Lu Sun Xiaona Li Mingyue Zhang Liangtian Wan Yun Lin Xianpeng Wang Gang Xu Department of Communication Engineering Institute of Information Science TechnologyDalian Maritime UniversityDalian116026China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and TelecommunicationsChongqing400065China Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province DUT School of Software Technology&DUT-RU International School of Information Science and EngineeringDalian University of TechnologyDalian 116620China College of Information and Communication Engineering Harbin Engineering UniversityHarbin 150001China School of Information and Communication Engineering Hainan UniversityHaikou 570228China State Key Laboratory of Millimeter Waves School of Information Science and EngineeringSoutheast UniversityNanjing 210096China

Interconnection of all things challenges the traditional communication methods,and Semantic Communication and Computing(SCC)will become new *** is a challenging task to accurately detect,extract,and represent semantic information in the research of SCC-based *** previous research,researchers usually use convolution to extract the feature information of a graph and perform the corresponding task of node ***,the content of semantic information is quite *** graph convolutional neural networks provide an effective solution for node classification tasks,due to their limitations in representing multiple relational patterns and not recognizing and analyzing higher-order local structures,the extracted feature information is subject to varying degrees of ***,this paper extends from a single-layer topology network to a multi-layer heterogeneous topology *** Bidirectional Encoder Representations from Transformers(BERT)training word vector is introduced to extract the semantic features in the network,and the existing graph neural network is improved by combining the higher-order local feature module of the network model representation network.A multi-layer network embedding algorithm on SCC-based networks with motifs is proposed to complete the task of end-to-end node *** verify the effectiveness of the algorithm on a real multi-layer heterogeneous network.

关键词： Semantic communication and computing Multi-layer network Graph neural network Motif

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An Enhanced-Precision Bandgap Reference Incorporating Temperature-Compensation 3

An Enhanced-Precision Bandgap Reference Incorporating Temper...

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3rd International Symposium on Semiconductor and Electronic Technology, ISSET 2024

作者： Xiang, Tianzi Xu, Quanyuan Liu, Haorui Lu, Ying Southwest Forestry University College of Big Data and Intelligent Engineering Yunnan Kunming China Southwest Forestry University Key Laboratory of Forestry and Ecological Big Data State Forestry Administration Yunnan Kunming China Changchun University of Science and Technology School of Physics Jilin Changchun China

ISBN: (纸本)9798350390285

In this work, an Enhanced-Precision Bandgap Reference Incorporating Temperature-Compensation was proposed. Unlike traditional bandgap references, the reference voltage output of 0.701 V is realized by combining reference current and resistor. Meanwhile, an innovative Linear-piecewise current compensation was proposed, which aimed at optimizing the temperature drift of the bandgap reference. The simulation results shows that the proposed curvature-compensation technique improves the TC from 24.96 to 2.14 ppm°C in the temperature range of -40 °C to 120 °C;the PSRR of Enhanced-Precision Bandgap Reference at low frequency is 72.40 dB;the line regulation is about 0.08 %/V for input voltages between 3.2 and 3.7 V in standard 0.18 μm 1P6M CMOS process. The proposal of this new circuit provides a new idea for the realization of high- performance integrated circuits in the future. © 2024 IEEE.

关键词： Piecewise linear techniques

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Mechanical properties of thermoelectric generators

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Journal of Materials Science & Technology 2023年第17期148卷 64-74页

作者： Xin Bao Shuaihang Hou Zhixin Wu Xiaodong Wang Li Yin Yijie Liu Huolun He Sichen Duan Baolin Wang Jun Mao Feng Cao Qian Zhang School of Materials Science and Engineering and Institute of Materials Genome&Big DataHarbin Institute of TechnologyShenzhen518055China State Key Laboratory of Advanced Welding and Joining Harbin Institute of TechnologyHarbin150001China School of Science Harbin Institute of TechnologyShenzhen518055China

To satisfy the requirements of practical applications,thermoelectric generators should be highly efficient and mechanically ***,progress in designing high-performance thermoelectric generators has been ***,the mechanical properties of thermoelectric generators are still *** this review,studies on the mechanical properties of thermoelectric generators are *** me-chanical properties of bulk thermoelectric generators will be first *** this section,the mechan-ical properties of thermoelectric materials and the strategies for improving their mechanical properties are *** the device’s failure usually occurs at the interface between the thermoelectric ma-terials and electrode,the joint strength of electrodes and thermoelectric materials will be *** that,the mechanical properties of the inorganic thin-film thermoelectric devices will be *** the figure of merit for the flexibility of thermoelectric materials depends on the film thickness,elastic modulus,and yield strength,the synthesis methods of thin-film thermoelectric materials will be ***,this review will be concluded with a discussion on flexible organic thermoelectric de-vices and flexible devices using bulk legs.

关键词： Thermoelectric generator Mechanical properties Interface Joint strength

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Segmentation and Detection of Industrial Surface Defect Based on Deep Neural Network 7th

Segmentation and Detection of Industrial Surface Defect Base...

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7th International Conference on Computing, Control and Industrial engineering, CCIE 2023

作者： Hou, Yi Qian, Songrong School of Mechanical Engineering Guizhou University Guiyang550025 China State Key Laboratory of Public Big Data Guizhou University Guiyang550025 China

ISBN: (纸本)9789819927296

In the manufacturing of industrial products, traditional methods of surface inspection do not meet the increasing quality standards in manufacturing. In this paper, first, we propose a lightweight convolutional neural network framework for the detection of subtle surface defects using deep neural network algorithms. The detection framework consists of two stages: pixel-based defect segmentation and defect classification, where the defect segmentation algorithm segments and extracts the defect-containing areas of an industrial image, and the defect classification algorithm predicts whether the image contains defects based on the results of the previous segmentation. Then, we improved the You Only Look Once (YOLO) algorithm for industrial surface defect detection. The improved algorithm, defined as DR-YOLO, identifies defects and their approximate locations from multi-target images. Subsequently, we use data augmentation to address the problems of small sample size and uneven distribution. The experimental results show that our method outperforms other detection algorithms in terms of evaluation metrics, as well as being more advantageous in terms of rapidity of detection, and the results of defect segmentation are close to industrial reality. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Pixels

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Efficient Deep learning-based Real-time Migration Strategies for Containers in SDN 6

Efficient Deep learning-based Real-time Migration Strategies...

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6th International Conference on Next Generation data-Driven Networks, NGDN 2024

作者： Huang, Shan Liu, Yanan Li, Yanuo He, Feili Duan, Xiaodong School of Computer Science and Engineering Dalian Minzu University Dalian China Key Laboratory of Big Data Application Technology of the State Ethnic Affairs Commission Dalian China Dalian Ethnic Culture Digital Technology Key Laboratory Dalian China

ISBN: (纸本)9798350388374

Container technology is widely used and improve the efficiency of container real-time migration has become an important research topic. Existing studies mainly focus on optimizing iterative dumping of containers without fully considering the impact of migration node selection on migration efficiency. Software-defined networking (SDN), achieves centralized control of the network by separating the control plane and the forwarding plane. Applying deep learning to SDN makes tasks such as traffic prediction and network optimization more intelligent. This paper proposes Prediction Model based on CLCB (CNN-LSTM-CBAM) uses real-time collected load data as input, extracts feature components of load data using one-dimensional convolutional neural networks, and then predicts using LSTM. To improve prediction accuracy, a lightweight CBAM attention module is introduced, and the prediction results are output through fully connected layers. The Network Traffic Prediction Model based on GCGRU-Conv (GCN-GRU-CNN) considers both spatial and temporal features of network traffic. GCN and GRU are used to extract spatial and temporal features of network traffic, respectively, and then these features are fused together using convolutional neural networks. To address the gradient problem caused by increasing network depth, a residual network structure is introduced. Experimental results show that the method proposed can effectively reduce the container migration time and has better prediction results. © 2024 IEEE.

关键词： Convolutional neural networks

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Untangling the association between urban mobility and urban elements

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地球空间信息科学学报（英文版） 2024年第4期27卷 1071-1089页

作者： Jinzhou Cao Wei Tu Rui Cao Qili Gao Guanzhou Chen Qingquan Li College of Big Data and Internet Shenzhen Technology UniversityShenzhenChina Key Laboratory of National Geographic Census and Monitoring Ministry of Natural ResourcesWuhan UniversityWuhanChina School of Architecture and Urban Planning & Guangdong Key Laboratory of Urban Informatics Shenzhen UniversityShenzhenChina Department of Land Surveying and Geo-Informatics & Smart Cities Research Institute The Hong Kong Polytechnic UniversityHong KongChina State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan UniversityWuhanChina

Understanding complex urban systems necessitates untangling the relationships between diverse urban elements such as population,infrastructure,and socioeconomic *** laws are basic but effective rules for evaluating a city's internal growth logic and assessing its efficiency by investigating whether urban indicators scale with *** date,only limited research has empirically explored the scaling relations between variables of urban mobility in mega-cities at an intra-urban scale of a few *** multiple urban-sensed and human-sensed data,this study proposes a thorough framework for quantifying the scaling laws in a *** begin,urban mobility networks are built by aggregating population flows using large-scale mobile phone tracking *** demonstrate the spatiotemporal variability of urban mobility,various network-based mobility measures are *** that,three different features of urban mobility laws are exposed,explaining spatial agglomeration,spatial hierarchical structures,and the temporal growth *** scaling correlations between urban indicators pertaining to socioeconomic features and infrastructure and a mobility-population measure are then quantified using multi-sourced urban-sensed *** this framework to the case study of Shenzhen,China revealed(a)spatial travel heterogeneity,hierarchical spatial structures,and mobility growth,and(b)not only a robust sub-linear relationship between infrastructure volume and population,but also a sub-linear relationship for socioeconomic *** identified scaling laws,both in terms of mobility measures and urban indicators,provide a multi-faceted portrait of the spatio-temporal varia-tions of urban settings,allowing us to better understand intra-urban developments and,consequently,provide critical policy evaluations and suggestions for improving intra-urban efficiency in the future.

关键词： Scaling law urban scaling urban mobility intra-urban system mobile phone data

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