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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...

引用

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

A Predictive Framework for Shipborne Wind Speed Measurement ...

引用

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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network Topology Overlapping Group Detection Based on h-Core Pruning 6

Network Topology Overlapping Group Detection Based on h-Core...

引用

6th IEEE International Conference on Intelligent computing and Signal Processing, ICSP 2021

作者： Zhang, Qi Liu, Yan Cai, Yuheng Zhong Yuan Network Security Research Institute Zhengzhou University Zhengzhou China Pla Strategic Support Force Information Engineering University State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9780738143705

The network group detection has a large number of application researches and has achieved certain results in network security, IP entity location and load balancing. However, due to the large scale of the network topology graph and the large number of terminal nodes, most of the existing overlapping group detection algorithms are relatively complex, and the data size that can be processed is limited, and cannot be directly applied to the network topology. This paper proposes a network topology overlapping group detection algorithm based on h-core pruning. This algorithm decomposes the network into smaller strong connected components, reduces the interference of noisy nodes to overlapping group discovery, and more efficiently detect the largest k-plex group on the target network. A large number of experiments show that the method in this paper is effective. Compared with the full-enum algorithm, our method increases the network size that can be handled by the largest k-plex group by two orders of magnitude, and can detect overlapping groups in the network topology more efficiently. © 2021 IEEE.

关键词： network topology

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A DoS-attack Resilient Control Approach for AIOT-enabled Systems

A DoS-attack Resilient Control Approach for AIOT-enabled Sys...

引用

Artificial Intelligence of Things and Systems (AIoTSys), International Conference on

作者： Xiaoya Cao Zhenya Chen Weisheng Liu Yuanlong Liu Wenting Wang Xin Wang Ming Yang 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 Grid Shandong Electric Power Company Jinan China State Grid Shandong Electric Power Research Institute Jinan China

This paper presents a novel controller design for dealing with the challenges posed by Denial-of-Service (DoS) attacks in the context of Artificial Intelligence of Things (AIOT). The proposed design employs a prediction approach that utilizes uniform quantization for both encoder and decoder, aiming to effectively mitigate data loss during data transmission in scenarios involving DoS attacks. Furthermore, the study investigates the application of dynamic system design as a means to enhance the security defense mechanisms of AIOT, consequently bolstering the overall resilience of the system. The effectiveness and practicality of the proposed approach are validated through comprehensive simulations, providing empirical evidence of its efficacy.

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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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Research and Implementation of Electric Supply Reliability Evaluation Model for Distribution network

Research and Implementation of Electric Supply Reliability E...

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information and Communication Technology (ICTech), International Conference of

作者： Junfeng Qiao Chenhong Huang Aihua Zhou Hua Gu Lin Peng Xingde Huang Xiaofeng Shen Jingde Chen State grid key laboratory of information Network Security State Grid Smart Grid Research Institute Co.LTD Nanjing Jiangsu China Qingpu Power Supply Company State Grid Shanghai Electric Power Company Shanghai China

The scale of distribution network is expanding, the construction of distribution automation and ubiquitous power Internet of Things is advancing, and the multi-level and multisource real-time operation monitoring data of distribution network is growing exponentially. On the one hand, big data provides data support for distribution network reliability evaluation and dispatching planning, in different aspects, this also raises other requirements for the calculation accuracy and speed of reliability evaluation. How to mine the factors that have a great impact on reliability and make better use of electric data analysis algorithm to predict the changes of distribution network reliability in the future time scale is the focus of research. This article mainly taking technical breakthrough in electric distribution network data analysis, and studies its reliability evaluation methods of electric data analysis from both recent and forward aspects. First of all, the source of distribution network big data is analyzed from the internal and external aspects of the power system, and the data, data formats and relationships among the data source systems are analyzed. The characteristics of distribution network big data, such as wide source, diverse structure, fine granularity and complex relationships, are obtained in many ways. In view of the characteristics of multi-source and heterogeneous distribution network data, this paper puts forward a data processing algorithm based on multi-source data and a truth value discovery algorithm based on block coordinate descent method, which find out a new approach to overcome the difficulty of inconsistency and conflict of multi-source monitoring data.

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Channel Drop Out: A Simple Way to Prevent CNN from Overfitting in Motor Imagery Based BCI 7th

Channel Drop Out: A Simple Way to Prevent CNN from Overfitti...

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7th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2021

作者： Luo, Jing Wang, Yaojie Xu, Rong Liu, Guangming Wang, Xiaofan Gong, Yijing Shaanxi Key Laboratory for Network Computing and Security Technology School of Computer Science and Engineering Xi’an University of Technology Xi’an China Shaanxi Province Institute of Water Resources and Electric Power Investigation and Design Xi’an China

ISBN: (纸本)9789811659393

With the development of deep learning, many motor imagery brain-computer interfaces based on convolutional neural networks (CNNs) show outstanding performances. However, the trial number of EEG in the training set is usually limited, and redundancy extensively exists in multiple channel EEG. Thus, overfitting often appears in CNN based motor imagery recognition model and greatly affects the performances of model. In this paper, channel drop out is proposed to address this problem by data augmentation and ensemble learning. Specifically, one of all EEG channels will be dropped and replaced by the mean signal of all EEG channels. In this way, the trial number in the training set was enlarged by channel drop out. And at the testing stage, all the EEG trials processed by channel drop out were fed to the CNN model and the average output probabilities of them were applied to determine the prediction. The experiments were conducted on two popular CNN models applied in motor imagery BCI and BCI Competition IV datasets 2a to verify the performances of the proposed channel drop out approach. The results show that average improvements provided by channel drop out in two-category or four-category motor imagery classification are 2.83% and 2.65% compared with the original CNN model. So the channel drop out approach significantly improves the performances of motor imagery based BCI. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Brain computer interface

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