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Research on Electricity Consumption Model of Library Building Based on data Mining

Energy engineering 2022年第6期119卷 2407-2429页

作者： Jiaming Dou Hongyan Ma Rong Guo School of Electrical and Information Engineering Beijing University of Civil Engineering and ArchitectureBeijing102616China Institute of Distributed Energy Storage Safety Big Data Beijing University of Civil Engineering and ArchitectureBeijing102616China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing102616China

With the exponential development of Chinese population,the massive energy consumption of buildings has recently become an interest *** much research has been conducted on residential buildings,heating ventilation and air conditioning(HVAC),little research has been conducted on the relationship between student’s behavior,campus buildings,and their *** classical seasonal decomposition,hierarchical clustering,and apriori algorithm,this paper aims to provide an empirical model for consumption data in campus *** meter data from a library in Beijing,China,is adopted in this *** electricity consumption patterns are investigated on an hourly/daily/monthly *** to the monthly analysis,electricity consumption peaks each year around June and December due to teaching programs,social exams,and outdoor *** data analysis revealed a relatively stable consumption *** shows three different types of daily load *** data analysis demonstrated a high relationship between HVAC consumption and building total consumption,with a lift value of ***,links between temperature and subsystems were also *** a case study of library,this study provides a unique insight into campus electricity *** results could help to develop operational strategies for campus facilities.

关键词： Electricity consumption data mining load profile campus building

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Localized knowledge Distillation Helps IoT Devices Provide High-performance Visual Services

Localized Knowledge Distillation Helps IoT Devices Provide H...

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2023 IEEE International Conference on Web Services, ICWS 2023

作者： Yu, Jinhui Ding, Chuntao Li, Huifang Jin, Yi Li, Yidong Key Laboratory of Big Data and Artificial Intelligence in Transportation Ministry of Education China Beijing Jiaotong University School of Computer and Information Technology China China Unicom Research Institute China

ISBN: (纸本)9798350304855

Deploying high-performance convolutional neural networks (CNNs) on ubiquitous Internet of Things (IoT) devices to provide convenient services has attracted increasing attention. However, most existing studies have two limitations, (i) low performance due to lack of effective learning strategies;(ii) difficulty to measure resource consumption due to lack of real deployment. To this end, this paper proposes a novel localized knowledge distillation (LKD) method to train the resource-efficient CNN and implements a cloud-assisted system to evaluate the on-device performance. The proposed LKD follows the layer-wise heterogeneous information distribution in the CNN and distills the knowledge from features that contains the most crucial knowledge to the resource-efficient CNN. Thus, the knowledge guided to learn by limited parameters is reduced to the crucial part, which is more reliable for the resource-efficient CNN. The cloud-assisted system consists cloud server and IoT devices, which allows the training and deployment of the resource-efficient CNN, and the measurement of the corresponding resource consumption. Experimental results show that the proposed LKD could improve the performance on standard benchmarks close to the counterpart CNN, and the robustness on corrupted data by approximately 11.3% for the resource-efficient CNN. The measurements on the cloud-assisted system also demonstrate the resource efficiency for transmission and on-device running. © 2023 IEEE.

关键词： Distillation

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Multivariate Time Series Classification via Hierarchical Graph Embedding

Multivariate Time Series Classification via Hierarchical Gra...

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International Joint Conference on Neural Networks (IJCNN)

作者： Wenhao Niu Xingrui Zhuo Gongqing Wu JunWei Lv Zan Zhang Chenyang Bu Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) School of Computer Science and Information Engineering Hefei University of Technology Anhui China

Multivariate time series classification aims to determine the labels for multivariate time series samples. Although variable interaction relationships and sample similarity relationships exist in multivariate time series, the available related methods usually ignore the rich relationships and are ineffective in exploiting these. To solve this problem, we propose a Hierarchical Graph Embedding for Multivariate Time Series Classification (MTSC-HGE), which consists of a variable-wise attentive graph pooling module and a sample-wise graph convolutional module to obtain the relationships of variables and samples. Specifically, we design an attentive graph pooling module based on self-attention, which can obtain sample features fusing temporal patterns and variable interaction relationships in samples. Furthermore, we propose a graph mapping criterion that converts the MTS dataset into a graph based on dynamic time warping to explicitly reflect the similarity relationships between samples. To capture latent sample relationships, a GCN module is utilized on the sample graph to integrate sample features obtained from the attentive graph pool module. In addition, a classifier takes the rich representation output by the model to get the final predicted class. Extensive experiments on 14 public datasets show that MTSC-HGE significantly outperforms state-of-the-art baselines.

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knowledge Enhanced Coreference Resolution via Gated Attention

Knowledge Enhanced Coreference Resolution via Gated Attentio...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： He, Kai Mao, Bing Zhou, Xiangyu Li, Yufei Gong, Tieliang Li, Chen Wu, Jialun Xi'an Jiaotong University School of Computer Science and Technology Xi'an China Xi'an Jiaotong University Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi'an China

ISBN: (纸本)9781665468190

Coreference resolution aims at linking all mentions that refer to the same entity, which are widely adopted in many biomedical and bioinformatics tasks, such as biomedical knowledge graph construction and metabolic pathway integration. Many recent studies focus on improving neural model structures. However, we argue that a practical method that integrates commonsense knowledge can further improve coreference resolution performance, because commonsense delivers extra prior knowledge for reasoning and can enhance related representations, rather than naive mention-context occurrence modeling. In this work, we propose an effective method to integrate external commonsense knowledge into a neural coreference resolution model. Specially, a gated attention mechanism is employed in our method to leverage commonsense according to different contexts. By using ConceptNet as the knowledge base in three span-ranking backbone models, the models can yield significant performance gains on used datasets. We also achieve improvements in tasks of long-term mention detection and cross-sentence coreferences after incorporating knowledge. © 2022 IEEE.

关键词： Model structures

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Research on expressway traffic flow prediction model based on MSTA-GCN

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Journal of Ambient Intelligence and Humanized Computing 2023年第7期14卷 9317-9328页

作者： Ning, Tao Wang, Jiayu Duan, Xiaodong Institute of Computer Science and Engineering Dalian Minzu University Dalian116000 China Big Data Application Technology Key Laboratory of State Ethnic Affairs Commission Dalian Minzu University Dalian116000 China Institute of Information Dalian Maritime University Dalian116021 China

The traffic flow prediction of expressway is highly nonlinear and complex, so it is technically and operationally difficult to predict it accurately. In order to predict the traffic flow of expressway and alleviate its traffic congestion, a multi-component spatial–temporal graph convolution network model based on attention mechanism (MSTA-GCN) is proposed, which combines GCN, standard CNN and attention mechanism. Firstly, near period, daily period and weekly period are modeled to capture the nonlinear correlation characteristics in multivariable urban spatial–temporal series. Then input the sample data into the integrated model for training and extract the characteristics of traffic flow data. Finally, the model is tested on California Expressway PeMS D7 dataset. The experimental results show that MSTA-GCN is superior to classical shallow learning model, benchmark deep learning model and other traffic flow deep learning models, and has lower prediction error. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Convolution

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DDoS Detection and Defense Based on FLAD and SDN 20

DDoS Detection and Defense Based on FLAD and SDN

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20th International Conference on Natural Computation, Fuzzy Systems and knowledge Discovery, ICNC-FSKD 2024

作者： Dong, Jie Fang, Wenyu Zheng, Wanling Liu, Jinkun Liu, Yanhua College of Computer and Data Science Fuzhou University Fuzhou350108 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou350108 China

ISBN: (纸本)9798350356328

In order to achieve more efficient and accurate DDoS detection while ensuring data privacy, this paper proposes a DDoS detection method based on FLAD. Firstly, this paper uses the FLAD algorithm to train a global DDoS detection model without leaving local traffic data, protecting the privacy and security of traffic data between different hosts, and improving aggregation efficiency by dynamically adjusting the aggregation weights to adapt to different sub-dataset increments. Secondly, a DDoS traffic detection method based on the integration of LSTM and CNN is proposed, which extracts and analyzes the temporal correlation of traffic data by calculating the statistical characteristics of traffic data within a time period, to achieve real-time detection of traffic feature data. Again, combined with the concept of SDN, real-time defense against DDoS based on ODL-API is implemented, and precise matching of DDoS detection results with network entity information is achieved, realizing the technology of real-time and precise issuance of multiple flow rules, effectively blocking DDoS malicious attack traffic, protecting important entities in the topology, and maintaining stable traffic in the topology. This paper focuses on solving the detection problem of DDoS traffic data increments and uneven data distribution through the FLAD algorithm. Experimental results show that the proposed method improves the accuracy of DDoS attack detection by more than 4% and the F1 Score by more than 7% compared to the FedAvg aggregation algorithm. © 2024 IEEE.

关键词： Differential privacy

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Hydrological Time Series Motif Association Rule Mining Based on Three-Step Pruning and Constraints 19

Hydrological Time Series Motif Association Rule Mining Based...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Zhu, Yuelong Teng, Zhixin Wu, Yirui Feng, Jun Hohai University Key Laboratory of Water Big Data Technology of Ministry of Water Resources School of Computer & Information Nanjing China

ISBN: (纸本)9798350346558

With the rapid development of big data technology, how to mine useful association rules from a large amount of time series data is an important task in the field of water resources. Mining potential association rules between time series modalities has become a hot topic, which will provide effective help for water governance to predict future hydrological trends. The traditional method of mining hydrological data has two drawbacks: Firstly, the traditional method focuses more on the similarity of modalities in the process of modal mining, so it is easy to mine similar modalities and instances in large-scale data, thus generating redundancy;secondly, the traditional method ignores asynchronous rules in the mining of temporal association rules, and this will lose many valuable rules. In this paper, we propose a three-step pruning method for modal mining, which improves on the MASS algorithm. We propose a constraint-based association rule mining algorithm that achieves asynchronous rule mining by introducing a minimum overlap threshold and a maximum time interval. Finally, we conducted experiments on the hydrological dataset. Compared with other methods, the experimental results show that the method proposed in this paper is effective. © 2022 IEEE.

关键词： Association rules

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An Effective Security Comparison Protocol in Cloud Computing

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computers, Materials & Continua 2023年第6期75卷 5141-5158页

作者： Yuling Chen Junhong Tao Tao Li Jiangyuan Cai Xiaojun Ren State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang550000China Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic TechnologyGuilin550000China POWERCHINA Guizhou Electric Power Engineering Co. Ltd.Guiyang550000China Blockchain Laboratory of Agricultural Vegetables Weifang University of Science and TechnologyWeifang261000China

With the development of cloud computing technology,more and more data owners upload their local data to the public cloud server for storage and *** this can save customers’operating costs,it also poses privacy and security *** challenges can be solved using secure multi-party computation(SMPC),but this still exposes more security *** cloud computing using SMPC,clients need to process their data and submit the processed data to the cloud server,which then performs the calculation and returns the results to each *** client and server must be *** there is cooperation or dishonest behavior between clients,some clients may profit from it or even disclose the private data of other *** paper proposes the SMPC based on a Partially-Homomorphic Encryption(PHE)scheme in which an addition homomorphic encryption algorithm with a lower computational cost is used to ensure data comparability and Zero-knowledge Proof(ZKP)is used to limit the client’s malicious *** addition,the introduction of Oblivious Transfer(OT)technology also ensures that the semi-honest cloud server knows nothing about private data,so that the cloud server of this scheme can calculate the correct data in the case of malicious participant models and safely return the calculation results to each ***,the security analysis shows that the scheme not only ensures the privacy of participants,but also ensures the fairness of the comparison protocol data.

关键词： Secure comparison protocols zero-knowledge proof homomorphic encryption cloud computing

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An Efficient Customized Blockchain System for Inter-Organizational Processes

An Efficient Customized Blockchain System for Inter-Organiza...

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2023 IEEE International Conference on Web Services, ICWS 2023

作者： Wang, Puwei Sun, Zhouxing Li, Rui Chen, Jinchuan Gong, Ping Du, Xiaoyong Renmin University of China School of Information China Renmin University of China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education China Renmin University of China School of Mathematics China Fujian Normal University College of Computer and Cyber Security China

ISBN: (纸本)9798350304855

Blockchain technologies pave a promising way for implementing the inter-organizational processes. Most of the current research works translate the execution logic in the process models into the smart contracts, which can run independently on the blockchain without the outside process engine. However, the works usually suffer from the execution and storage costs, since the translation needs to be done when the processes are deployed. In this paper, we customize a process engine for executing the inter-organizational business processes via a blockchain-style procedure, i.e., checking the validity of transactions, adding the valid transactions into the blockchain through the consensus mechanism, and then updating the process states according to the committed transactions. And then, we build a blockchain system by embedding the customized process engine into the blockchain nodes. Moreover, in order to realize the interactions between the inter-organizational processes running on blockchain and the services outside blockchain, we propose a blockchain-based approach for service registration, binding and invocation, and design a lease-based concurrency control protocol to logically isolate transactions from each other when invoking the services simultaneously. Finally, we implement a prototype system based on a permissioned blockchain platform Hyperledger Fabric and a process engine Activiti. The experimental results show the proposed blockchain system can execute the inter-organizational processes correctly and efficiently. © 2023 IEEE.

关键词： Blockchain

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Improved Power Conversion Efficiency and Stability of Perovskite Solar Cells Induced by Dual Interface Passivation

Improved Power Conversion Efficiency and Stability of Perovs...

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2023 International Conference on Optoelectronic Materials and Devices, ICOMD 2023

作者： Guan, Nianci Han, Qizhuo Institute of Biomedical Materials & Engineering Northwestern Polytechnical University Xi'an710072 China The School of Computer Science National Engineering Laboratory for Integrated Aero-Space-Ground- Ocean Big Data Application Technology Shaanxi Provincial Key Laboratory of Speech and Image Information Processing Northwestern Polytechnical University Xi’an710072 China

ISBN: (纸本)9781510674509

Efficient and stable Perovskite solar cells (PSCs) have been successfully produced using a dual-layer modified (DLM) perovskite thin film. This method simultaneously modifies both the bottom (SnO2/perovskite) and top (perovskite/SpiroOMeTAD) interfaces of the perovskite. The crystallinity of the perovskite film is enhanced after the DLM treatment. Additionally, thin barrier layers are formed on both its bottom and top surfaces, which reduce surface defect density and non-radiative recombination of carriers. The bottom and top modifier layers prevent direct contact between the hole transport layer and the electron transport layer, forming a high-energy barrier that promotes carrier transmission in PSCs. Consequently, the methylammonium acetate (MAAc)-PSC achieves an efficiency of 21.96%. © 2024 SPIE. All rights reserved.

关键词： Perovskite solar cells

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