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Research on Classroom Behavior Target Detection Algorithm based on YOLOv7

Research on Classroom Behavior Target Detection Algorithm ba...

引用

IEEE Advanced information Management,Communicates,Electronic and Automation Control Conference (IMCEC)

作者： Wenke Qiu Xiaoming Wu Xiangzhi Liu Computer Science and Qilu University of Technology (Shandong Academy of Sciences) Jinan ShanDong China 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 Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350316537

ISBN: (纸本)9798350316544

The integration of video information for the purpose of detecting students’ behavior in the classroom, with the aim of enhancing teaching efficiency and maintaining a stable classroom environment, offers significant assistance. However, existing models usually struggle to achieve good accuracy due to the lack of datasets, dense targets, interclass occlusion, and other issues. To address these difficulties, the CD-YOLO classroom behavior detection algorithm is proposed in this paper. And the classroom behavior dataset (DSL-Dataset) is created. We propose a loss function named VAIoU, which can be combined with vector ratio to filter the prediction box. To further improve the detection accuracy of occluded targets, we propose an attention module CM module with command feature fusion. Moreover, in this paper, we improve the convolutional kernel to design a lightweight convolutional kernel FFconv. Experimental results on DSL-Dataset show that CD-YOLO outperforms existing one-stage detection models in terms of detection accuracy.

关键词： Accuracy Education Object detection Filtering algorithms Feature extraction Vectors information management

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Demand Response Scheme: Achieving Sustainability in Distributed Data Processing

Demand Response Scheme: Achieving Sustainability in Distribu...

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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Xueying Zhai Yunfeng Peng Xiuping Guo Liang Zhang Wei Zhang School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China School of Economics and Management Beijing University of Posts and Telecommunications Beijing China Guizhou Key Laboratory of Electric Power Big Data Guizhou Institute of Technology Guizhou China 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

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

The rapid development of Internet of Things (IoT) technology has driven the intelligent transformation of various industries, including manufacturing, agriculture, and healthcare, etc., significantly enhancing their management capabilities and decision-making efficiency. Data analysis is crucial, serving as both the core technology of this transformation and a major driving force for advancing industry intelligence. However, the increasing volume of data has led to high energy consumption during data analysis, posing significant challenges to environmental and economic sustainability. Integrating renewable energy sources (RESs) is an effective way to reduce greenhouse gas emissions due to their low-carbon and renewable nature. Additionally, the spatiotemporal variability and complementarity of RES generation and grid electricity prices provide favorable conditions for distributed data processing. Therefore, a two-stage demand response scheme is proposed to reduce carbon emissions and electricity costs by prioritizing data processing in data centers (DCs) where RES is abundant or electricity prices are low, based on RES generation and electricity price signals. Simulation results show that the proposed scheme reduces 36.37% carbon emissions and 20.49% electricity costs compared to the benchmark algorithm, indicating its potential for both environmental and economic optimization.

关键词： Renewable energy sources Energy consumption Costs Electricity Simulation Carbon dioxide Spatiotemporal phenomena Internet of Things Sustainable development Optimization

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Location and Lightpath Selection in Multi-Domain Optical Data Center networks

Location and Lightpath Selection in Multi-Domain Optical Dat...

引用

Mobile Internet, Cloud computing and information security (MICCIS), International Conference on

作者： Changle Xu Xu Zhang Chuan Feng Xiaoxue Gong School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China Institute of Intelligent Communication and Network Security Chongqing University of Posts and Telecommunications Chongqing China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9798350389906

ISBN: (纸本)9798350389913

In the era of booming fifth-generation fixed networks (F5G), the service demands for high bandwidth continue to grow. Meanwhile, in multi-domain network scenarios, service requests are vulnerable to physical layer attacks initiated by malicious clients. In this paper, a risk-aware heuristic algorithm is proposed to address the problem of lightpath provisioning in multi-domain elastic optical data center networks (EODCNs). Simulation results show that the blocking probability of the proposed algorithm is lower than the comparison algorithm, 8.2% and 8.8% lower in NSFNET topology and US-Backbone topology, respectively.

关键词： Integrated optics Multiplexing Data centers network topology Optical design Simulation Optical fiber networks

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Segment Routing based In-Band network Telemetry in IPv6 over Optical networks

Segment Routing based In-Band Network Telemetry in IPv6 over...

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Mobile Internet, Cloud computing and information security (MICCIS), International Conference on

作者： Wei Liu Xu Zhang Chuan Feng Xiaoxue Gong School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China Institute of Intelligent Communication and Network Security Chongqing University of Posts and Telecommunications Chongqing China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9798350389906

ISBN: (纸本)9798350389913

With the rise of automated network operations, network telemetry has become increasingly important. Optical networks play a key role in modern communication systems because of their high bandwidth, low latency, and low loss. To better manage and monitor Internet Protocol version 6 (IPv6) over optical networks, programmable data planes (PDP) are used to achieve flexible data collection. Meanwhile, to solve the scalability problem of the control plane in the software-defined network (SDN), segment routing over IPv6 (SRv6) is introduced. By programming the source node on the path, SRv6 wisely instructs packets to collect the appropriate telemetry data at the segment nodes to balance the trade-off between bandwidth usage and monitoring coverage. To efficiently achieve the observability of IPv6 over optical networks, we model the system and propose an SRv6-based in-band network telemetry (INT) algorithm. Simulation results verify the performance of our algorithm.

关键词： Protocols Simulation Bandwidth Optical fiber networks Routing Universal Serial Bus Topology

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Univariate Forecasting of time series data Based on Seq2Seq Model 20

Univariate Forecasting of time series data Based on Seq2Seq ...

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Proceedings of the 2020 4th International Conference on Electronic information Technology and Computer Engineering

作者： Yanwen Guo Tao Wu Yanbing Liu Yixin Jiang Department of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China School of Cybersecurity and Information Law Chongqing University of Posts and Telecommunications Chongqing China Chongqing Engineering Laboratory of Internet and Information Sccurity Chongqing University of Posts and Telecommunications Chongqing China Electric Power Research Institute CSG Guangdong Provincial Key Laboratory of Power System Network Security Guangzhou China

ISBN: (纸本)9781450387811

With the release of country's 2020 new infrastructure projects, the increasing demand for electricity in various projects has further promoted the intelligent upgrade of the power grid. The processing of grid data at the edge has become one of the future trends of next generation grid. In the field of power grid edge time series data prediction, the inability to obtain the variable characteristics of other systems and the uncertainty of the length of various edge input data, this paper proposes a Seq2seq prediction model based on a Bi-directional Long Short Term Memory (Bi-LSTM). Bi-LSTM can make full use of contextual information to achieve a more accurate prediction effect. The Seq2Seq model can process variable-length sequence data. In this paper, Seq2Seq model and BI-LSTM model are combined to solve the problem of uncertain edge length and univariate prediction. At the same time, in order to solve the information loss caused by Seq2Seq in the encoding stage, the Attention mechanism is introduced, it can compute the allocation of resources to more important tasks while addressing the problem of information overload. Through experimental research, it is found that the proposed ABSeq2Seq model is superior to other classic models in univariate prediction, and it has the better generalization for various types of data. We have proved its effectiveness in the single variable power prediction.

关键词： Time Series Bi-LSTM Seq2Seq Prediction Attention Mechanism Univariate

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A Predictive Framework for Shipborne Wind Speed Measurement Correction Based on Self-Supervised Contrastive Learning

A Predictive Framework for Shipborne Wind Speed Measurement ...

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International Conference on Computer Supported Cooperative Work in Design

作者： Jian Song Xiang Li Zhenqiang Zhang Zhigang Zhao Chunxiao Wang Shunfang Wu Suiping Qi Jialiang Lv Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Key Laboratory of Computing Power Network and Information Security 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 Institute of Oceanographic Instrumentation Qilu University of Technology (Shandong Academy of Sciences) Qingdao China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Accurate measurement of wind speed on maritime vessels is crucial for weather and sea condition forecasting, safe navigation, power generation, hydrological simulation, and other applications. However, the precision of measurements may be subject to certain errors due to factors such as vessel motion and environmental conditions. To enhance the precision of shipborne wind speed measurement, this paper introduces an innovative approach based on contrastive learning. Through proficient feature extraction and the application of a self-supervised contrastive learning algorithm, this method extracts features of varying granularity from marine observational data to predict and correct shipborne wind speed measurements. To the best of our knowledge, this study represents the first attempt to validate contrastive learning in the intelligent analysis of marine observational data. Validation experiment results demonstrate the outstanding performance of this method in both single-step and multi-step predictions, showcasing higher efficacy and robustness compared to alternative approaches.

关键词： Accuracy power measurement Wind speed Time series analysis Sea measurements Contrastive learning Predictive models

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APT traffic construction and detection method for virtual power plant

APT traffic construction and detection method for virtual po...

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4th Energy Conversion and Economics Annual Forum (ECE Forum 2024)

作者： Chang Liu Jin Wang Kailong Yu Qiuyu Lu June Li State Grid Hubei Electric Power Research Institute Wuhan People's Republic of China Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Wuhan People's Republic of China

ISBN: (数字)9781837242924

As an important infrastructure of carbon peaking and carbon neutrality goals, virtual power plant (VPP) is likely to be the target of advanced persistent threat (APT), at the same time, the lack of APT data of VPP makes it difficult to realize APT detection in VPP. Thus, this paper proposed an APT traffic construction and detection method for VPP. Firstly, transfer component analysis (TCA) is used to select similar traffic features in different domains and reduce the difference in data distribution, so as to construct a traffic dataset suitable for APT detection in VPP. Then, based on sequence generative adversarial nets (SeqGAN), data augmentation is carried out on minority class traffic to improve the detection performance of APT in corresponding stages, forward and reverse min-max normalization is introduced to reduce the computational complexity and improve the data augmentation ability of SeqGAN. Finally, an APT detection method based on information gain-adaptive boosting (IG-Adaboost) is proposed to further improve the overall detection performance of APT. The experimental results show that the proposed method can construct traffic data suitable for APT detection in VPP, realize effective detection of VPP’s APT, and achieve the highest detection accuracy of 98.47%.

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Deep Learning Enabled Bandpass Electromagnetic Metasurface Inverse Design

Deep Learning Enabled Bandpass Electromagnetic Metasurface I...

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Cross Strait Quad-Regional Radio Science and Wireless Technology Conference (CSRWTC)

作者： Shenghui Zhao Ruizhi Zhang Meng Guo Xuewei Zhang Lifeng Nie Xiaoyu Pang The Research Institute for Special Structures of Aeronautical Composite AVIC Jinan Shandong P.R.China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputing Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan Shandong P. R. China

ISBN: (数字)9798331507794

ISBN: (纸本)9798331507800

Conditional variational autoencoders (cVAE) is proposed to automate and optimize the design of band-pass electromagnetic metasurfaces. Electromagnetic responses curves and metasurface structural parameters are used as the input to train the deep neural work based cVAE model. The model is trained on ten thousand metasurface and transmission curve pairs, which are calculated using MIT Electromagnetic Equation Propagation (MEEP) software. The trained model takes transmission curves as input and then output metasurface structures, achieving a deep learning enabled inverse design process. By comparing the input transmission curve with the MEEP simulated curves of the generated metasurface, it is found that the model has high accuracy, giving a mean square error (MSE) value of 0.005 and R2 score of 0.967. The metasurface structures generated by cVAE also show good diversity, indicating that the cVAE captures the effective features in the latent space. Using the cVAE model, metasurface could be generated in less than one second using a single graphics processing unit (GPU), paving the way for fast, efficient and scalable inverse design solutions in the field of electromagnetic metasurfaces.

关键词： Deep learning Band-pass filters Wireless communication Training Autoencoders Graphics processing units Mean square error methods Metasurfaces Mathematical models Electromagnetics

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A Web Attack Detection Method Based on DistilBERT and Feature Fusion for power Micro-Application Server

A Web Attack Detection Method Based on DistilBERT and Featur...

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Advanced Electronics, Electrical and Green Energy (AEEGE), International Conference on

作者： Li Nige Chen Lu Zhao Lei Teng Zhenning Wang Zhiqiang Shao Yiyang Gui Xiaolin State Grid Smart Grid Research Institute Co. Ltd. State Grid Laboratory of Power cyber-Security Protection and Monitoring Technology Nanjing China Shaanxi Province Key Laboratory of Computer Network Faculty of Electronic and Information Engineering Xi’an Jiaotong University Xi’an China

The widespread use of power micro-applications has significantly expanded the attack surface of their server side, thus increasing the risk of vulnerability attacks such as SQL injection, XSS, and CRLF injection. To ensure the security of power systems, these attacks must be detected precisely and timely. Therefore, this paper proposes a server-side web attack detection method based on DistilBERT and feature fusion for HTTP and HTTPS requests. The method treats HTTP and HTTPS requests as text data, specifically, extracts deep semantic features using DistilBERT, and fuses them with well-designed empirical features to comprehensively characterize and classify the request. Consequently, anomalous requests can be detected. The experimental results show that the accuracy, precision, recall, and F1 score of the method on HTTP CSIC 2010 and FWAF datasets are close to or higher than 99%. Compared with other methods as far as we known, this method has better performance and efficiency.

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Performance Analysis for Cooperative Backscatter Communication Systems under Commensal Mode

Performance Analysis for Cooperative Backscatter Communicati...

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International Conference on Communication Technology (ICCT)

作者： Yingting Liu Yinghui Ye Jiaxiu Ma Tankun Wu Yongli Zhao State Grid Gansu Electric Power Research Institute Lanzhou China Shaanxi Key Laboratory of Information Communication Network and Security Xi'an University of Posts & Telecommunications Xi'an China School of Information Renmin University of China Beijing China College of Physics and Electronic Engineering Northwest Normal University Lanzhou China State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781665432078

In this letter, a cooperative backscatter communication system is considered, which consists of one primary transmitter, one primary receiver (PR), one backscatter device (BD) and one backscatter receiver. The considered system works in the commensal mode, in which the BD needs to adjust the reflection coefficient to satisfy the BR's requirement of successive interference cancellation. Based on the obtained distribution functions for the variable that is a product of the two variables with exponential distribution, the analytical expressions of the outage probability and ergodic capacity for the considered backscatter system are both derived. The numerical results verify the correction of our analytical results and exhibit that the derived results can evaluate the system performance more accurate compared to the existing results.

关键词： Communication systems Transmitters Exponential distribution System performance Receivers Probability power system reliability

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