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作者： Wenhao Liu Delong Han Mengmeng Sun Qianlong Liu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Qilu University of Technology (Shandong Academy of Sciences)

With the deepening development of a new round of global technological revolution and industrial transformation, the new generation of information technology is constantly breaking through. At the same time, the international environment is becoming increasingly complex, and competition in technology and industry is becoming more intense. Currently, China has shifted to a stage of high-quality development and is in a critical period of transforming its development mode, optimizing its economic structure,and transforming its growth momentum. As an important manufacturing province in China,Shandong has a complete range of industries and a complete structure. However, there are also constraints such as outdated traditional industries, surplus low-end manufacturing, and insufficient technological innovation. Therefore, promoting the digital and intelligent transformation and upgrading of the manufacturing industry has become an inevitable choice for industrial development. This article studies the current situation and practical difficulties of industrial intelligence empowering the development of the manufacturing industry, explores paths suitable for the upgrading of Shandong's manufacturing industry,and has certain reference value for promoting the intelligent transformation and highquality development of Shandong's manufacturing industry, and accelerating the construction of an advanced manufacturing strong province.

关键词： Industrial Intelligence Manufacturing Industry Industrial Upgrading Shandong Province

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Data-Driven Active Power Dispatch for Wind Farms

Data-Driven Active Power Dispatch for Wind Farms

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Data Driven Control and Learning Systems (DDCLS)

作者： Xuguo Jiao Daoyuan Zhang Xin Wang Zhaoxing Ma Zhenyong Zhang Wenfeng Liu School of Information and Control Engineering Qingdao University of Technology Qingdao China State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China School of Civil Engineering Qingdao University of Technology Qingdao China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Active power dispatch of wind farms plays an important role in power grid scheduling. In this paper, a data-driven active power dispatch strategy for wind farms is proposed, which uses the key point of minimizing the standard deviation of wind turbines' output power series to reduce their fluctuations. First, to build the optimization problem, the historical active power output data of each wind turbine is collected from the Supervisory Control And Data Acquisition (SCADA) system. Then, the objective function is developed based on the idea of minimizing the standard deviation of the output power time series. Meanwhile, considering the constraints of wind turbines' output power and active power balance of the grid, the feasible domain of the cost function is constructed. Further, to solve the proposed nonlinear optimization problem with equality and inequality constraints, the trust region interior point method (TRIPM) is employed to explore optimal solution within the feasible domain. Finally, actual data collected from wind farms is used to test effectiveness of the designed approach and experimental results show that our method can achieve better output power smoothing performance than conventional Proportional Dispatch (PD) method.

关键词： Fluctuations Time series analysis SCADA systems Power smoothing Wind farms Linear programming Power grids

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Communication-Efficient Federated Learning for Network Traffic Anomaly Detection

Communication-Efficient Federated Learning for Network Traff...

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International Conference on Mobile Ad-hoc and Sensor networks, MSN

作者： Xiao Cui Xiaohui Han Guangqi Liu Wenbo Zuo Zhiwen Wang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China Quancheng Provincial Laboratory Jinan China

ISBN: (数字)9798350358261

ISBN: (纸本)9798350358278

As an emerging machine learning method, Federated Learning is widely used in network anomaly detection scenarios. However, many current federated learning-based network anomaly detection works ignore the communication overhead problem during model training. The transmission of model parameters occupies a large amount of bandwidth, affecting the efficiency of network anomaly detection as well as the communication tasks of other programs. In this study, we propose eFedAD, an efficient federated learning framework for network traffic anomaly detection. Specifically, eFedAD uses singular value decomposition to compress the transmitted parameters and introduces a weighting mechanism to control the compression rate and aggregation proportion. In this way, eFedAD can significantly reduce the number of transmitted parameters and assign higher weights to more valuable clients, enhancing the generalization ability of the global model. Additionally, to address the slow convergence issue due to compressed parameters and minimize the total number of transmitted parameters, eFedAD uses a client selection method that is based on clustering clients’ data feature distributions. The experimental results demonstrate that eFedAD outperforms other compression methods and network anomaly detection approaches, achieving excellent performance.

关键词： Training Data privacy Federated learning Telecommunication traffic Data models Sensors Task analysis

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Fine-Grained Depth Knowledge Distillation for Cloth-Changing Person Re-identification

Fine-Grained Depth Knowledge Distillation for Cloth-Changing...

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International Joint Conference on Neural networks (IJCNN)

作者： Yuhan Yao Jiawei Feng Ancong Wu Jiangqun Ni Wei-Shi Zheng School of Computer Science and Engineering Sun Yat-sen University China School of Cyber Science and Technology Sun Yat-sen University Shenzhen China Department of New Networks Peng Cheng Laboratory Shenzhen China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China Guangdong Key Laboratory of Information Security Technology China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

The mission of cloth-changing person re-identification (CC-ReID) is to discover cloth-invariant and identity-related cues, while traditional person ReID methods rely on appearance features that are biased to cloth-related cues. To tackle this cloth-biased problem, many CC-ReID methods introduced auxiliary body shape information to extract cloth-invariant features, such as 2D sketch images or the 3D Skinned Multi-Person Linear (SMPL) model. However, 2D auxiliary information lacks 3D spatial features, while the 3D SMPL model encounters challenges in capturing features at a finer granularity due to manually defined parameters. To extract fine-grained 3D shape features, we estimate depth maps that contain richer shape information and propose a Fine-grained Depth feature Mining and Distillation (FDMD) framework. We introduce a depth branch and design a fine-grained local feature interaction module to mine fine-grained 3D body shape knowledge from estimated depth maps by exploring the context of semantic-aware local body-part features. To integrate cloth-invariant depth knowledge into the appearance features, the fine-grained 3D shape features are transferred to an appearance branch by feature-space-aligned distillation. Extensive experiments demonstrate that FDMD can achieve state-of-the-art performance on three widely used CC-ReID benchmarks PRCC, Celeb-reID and LaST.

关键词： Solid modeling Three-dimensional displays Shape Neural networks Benchmark testing Feature extraction Data mining

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Accelerating Mega-Scale Satellite Network Simulation in NS-3 via MPI-based Parallelization

Accelerating Mega-Scale Satellite Network Simulation in NS-3...

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IEEE International Conference on Communications (ICC)

作者： Haibin Song Tian Pan Guohao Ruan Yan Zheng Ying Wan Jiao Zhang Tao Huang Yunjie Liu Beijing University of Posts and Telecommunications Beijing China Purple Mountain Laboratories Nanjing China Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China China Mobile (Suzhou) Software Technology Co. Ltd Suzhou China

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Due to the high costs of low Earth orbit (LEO) satellite manufacturing and launch, as well as the complexity of in-orbit network protocol debugging, simulating and verifying satellite network protocols on the ground before satellite launch holds significant importance. Compared to the expensive emulation with one-to-one replication, simulation (e.g., using ns-3) can achieve discrete event processing at a relatively lower cost by extending the wall clock time. However, very few studies have used ns-3 for LEO satellite network simulation, facing challenges such as faithfully simulating the on/off state switching of inter-satellite links (ISLs) and achieving simulation performance scalability for high-density satellite constellations. In this work, we propose a system to accelerate mega-scale LEO satellite network simulation in ns-3 via MPI-based parallelization. Specifically, we simulate ISLs based on ns-3's P2P channels/P2P remote channels and achieve runtime link connection/disconnection by implementing stateful traffic dropping inside the network interface. Then, we conduct concurrent simulation with ns-3's parallel and distributed simulation capability and partition the satellite constellation into multiple simulation processes through a hierarchical clustering algorithm and automated scripts, considering satellite locality and inter-process workload balance. Our evaluation shows significant speed improvements via parallelization, e.g., a 373% speedup with 12 processes for LEO-192, and a 156% speedup with 3 processes for LEO-3072.

关键词： Satellite constellations Satellites Runtime Protocols Costs Low earth orbit satellites Clustering algorithms

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Joint 3D Deployment and Resource Allocation for UAV-assisted Integrated Communication and Localization

arXiv

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arXiv 2023年

作者： Bi, Suzhi Yu, Jiaxing Yang, Zheyuan Lin, Xiaohui Wu, Yuan The State Key Laboratory of Radio Frequency Heterogeneous Integration Shenzhen University Guangdong Shenzhen51800 China The Department of Broadband Communication Peng Cheng Laboratory Shenzhen518066 China The Department of Information Engineering The Chinese University of Hong Kong Hong Kong The State Key Laboratory of Internet of Things for Smart City The Department of Computer and Information Science University of Macau China

In this paper, we investigate an unmanned aerial vehicle (UAV)-assisted integrated communication and localization network in emergency scenarios where a single UAV is deployed as both an airborne base station (BS) and anchor node to assist ground BSs in communication and localization services. We formulate an optimization problem to maximize the sum communication rate of all users under localization accuracy constraints by jointly optimizing the 3D position of the UAV, and communication bandwidth and power allocation of the UAV and ground BSs. To address the intractable localization accuracy constraints, we introduce a new performance metric and geometrically characterize the UAV feasible deployment region in which the localization accuracy constraints are satisfied. Accordingly, we combine Gibbs sampling (GS) and block coordinate descent (BCD) techniques to tackle the non-convex joint optimization problem. Numerical results show that the proposed method attains almost identical rate performance as the meta-heuristic benchmark method while reducing the CPU time by 89.3%. Copyright © 2023, The Authors. All rights reserved.

关键词： Resource allocation

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A Novel Early Warning Model for Hand, Foot and Mouth Disease Prediction Based on a Graph Convolutional Network

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Biomedical and Environmental Sciences 2022年第6期35卷 494-503页

作者： JI Tian Jiao CHENG Qiang ZHANG Yong ZENG Han Ri WANG Jian Xing YANG Guan Yu XU Wen Bo LIU Hong Tu NHC Key Laboratory of Medical Virology and Viral Diseases National Institute for Viral Disease Control and PreventionChinese Center for Disease Control and PreventionBeijing 100026China Academy of Cyber Science and Engineering Southeast UniversityNanjing 211189JiangsuChina Center for Biosafety Mega Science Chinese Academy of SciencesWuhan 430071HubeiChina Guangdong Center for Disease Control and Prevention Guangzhou 511430GuangdongChina Shandong Center for Disease Control and Prevention Jinan 250014ShandongChina LIST Key Laboratory of Computer Network and Information Integration(Southeast University)Ministry of EducationSoutheast UniversityNanjing 211189JiangsuChina

Objectives Hand,foot and mouth disease(HFMD)is a widespread infectious disease that causes a significant disease burden on *** achieve early intervention and to prevent outbreaks of disease,we propose a novel warning model that can accurately predict the incidence of *** We propose a spatial-temporal graph convolutional network(STGCN)that combines spatial factors for surrounding cities with historical incidence over a certain time period to predict the future occurrence of HFMD in Guangdong and Shandong between 2011 and *** 2011-2018 data served as the training and verification set,while data from 2019 served as the prediction *** important parameters were selected and verified in this model and the deviation was displayed by the root mean square error and the mean absolute *** As the first application using a STGCN for disease forecasting,we succeeded in accurately predicting the incidence of HFMD over a 12-week period at the prefecture level,especially for cities of significant *** This model provides a novel approach for infectious disease prediction and may help health administrative departments implement effective control measures up to 3 months in advance,which may significantly reduce the morbidity associated with HFMD in the future.

关键词： HFMD Early warning model STGCN Disease prediction

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An Order Dispatch Algorithm for Ride-Hailing Based on Quantum Annealing and Multi-Agent Reinforcement Learning

An Order Dispatch Algorithm for Ride-Hailing Based on Quantu...

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Artificial Intelligence Technology (ACAIT), Asian Conference on

作者： Sumin Wang Tongyu Ji Chao Wang Key Laboratory of Specialty Fiber Optics and Optical Access Networks Shanghai University Shanghai China School of Information Engineering Gannan University of Science and Technology Ganzhou China School of Computer Engineering and Science Shanghai University Shanghai China

ISBN: (数字)9798331517090

ISBN: (纸本)9798331517106

The popularity of ride-hailing has grown significantly, making it one of the mainstream modes of transportation. Its core value and competitive advantage largely lie in the precise and efficient allocation of orders to drivers and passengers on the ride-hailing platform. In this paper, we use multi-agent reinforcement learning as the framework to accurately model and solve the state transition process in the dispatch area. For the problem of e-hailing order allocation, it is transformed into a quadratic unconstrained binary optimization (QUBO) model suitable for quantum annealing algorithm processing, and a reinforcement learning model is used to guide the quantum annealing QUBO model to provide multi vehicle order allocation decisions. Deploy the proposed algorithm on a simulation platform based on 100G e-hailing data from Chengdu City within a month, and conduct performance comparison experiments with Hungarian algorithm, greedy algorithm, and simulated annealing. The experimental results show that compared to the three algorithms mentioned above, the algorithm proposed in this paper can increase the average total reward of scheduling ride-hailing by 20% and the order acceptance rate by about 10%.

关键词： Greedy algorithms Annealing Urban areas Reinforcement learning Simulated annealing Quantum annealing Scheduling Robustness Resource management Vehicles

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WMLink: Wearable Smart Devices and Mobile Phones Linking through Passive Sniffing BLE and WiFi Signals

WMLink: Wearable Smart Devices and Mobile Phones Linking thr...

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International Conference on Advanced Cloud and Big Data (CBD)

作者： Naixuan Guo Zhaofeng Chen Heyang Xu Zhechun Zhao Yu Liu Sen Xu School of Information Engineering Yancheng Institute of Technology Yancheng China School of Computer Science and Engineering Southeast University Nanjing China Ministry of Education Key Laboratory of Computer Network and Information Integration (Southeast University)

ISBN: (纸本)9798350309720

In recent years, wearable smart devices have entered people’s lives. These devices can help users to record their daily activities, health data and so on. These devices periodically send out packets to keep connected with mobile phones, which can be observed by any external observer. By analyzing these packets, some information about the device owner will be revealed. Although the observer can sniff the wireless packets, the actual person using the device is unknown. In this paper, we link wearable smart devices with users’ mobile phones which are recognized as users’ electronic identity. Linking these two types of devices opens up new opportunities for applications such as authenticating users by multi-devices or constructing user device graph for advertisements. We deploy sniffing nodes to sniff BLE and WiFi signals, and put forward a multi-stage filtering method to reduce signals’ noises, then link two types of devices by an algorithm based on dynamic time warping. At last, our experiments show that 7 wearable devices can be linked with a correct rate more than 80%. We also find that when one device is observed more than 11 times, its linking accuracy can be close to 100%.

关键词： Meters Filtering Heuristic algorithms Wearable computers Observers Big Data Market research

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Design of an Intelligent Voice-Controlled Home System Based on Raspberry Pi

Design of an Intelligent Voice-Controlled Home System Based ...

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IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Kaiyi Wu Jiaying Li Jiabin Yu Yuhao Hua Binggang Xiao Wu Wang Key Laboratory of Electromagnetic Wave Information Technology and Metrology of Zhejiang Province College of Information Engineering China Jiliang University Hangzhou China School of Computer Science and Technology Xidian University Xi'an China Multisensory Integration Laboratory School of Psychological and Cognitive Sciences Peking University Beijing China

A complete intelligent voice-controlled home system should include voice capture, voice recognition, authentication, voice result processing, and other processes. Based on the intelligent voice interaction model of Raspberry Pi 3b+ and Alibaba Cloud platform, this paper realized a high recognition accurate, stable, and efficient intelligent voice-controlled home system. We used the Raspberry Pi to control the hardware interface, collected voice data through an external microphone, and performed command control with external sensors and controllable devices in the hardware part; The software was based on Alibaba Cloud intelligent voice interaction platform to realize voice data recognition. The system constructed can achieve the expected function after repeatedly testing the controlled device. The test results show that the recognition accuracy and recognition efficiency of the system's voice control instructions are both high in a relatively quiet environment.

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