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001468461 001__ 1468461
001468461 003__ OCoLC
001468461 005__ 20230707003252.0
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001468461 019__ $$a1380993600
001468461 020__ $$a9789819933006$$q(electronic bk.)
001468461 020__ $$a9819933005$$q(electronic bk.)
001468461 020__ $$z9789819932993
001468461 020__ $$z9819932998
001468461 0247_ $$a10.1007/978-981-99-3300-6$$2doi
001468461 035__ $$aSP(OCoLC)1381093510
001468461 040__ $$aEBLCP$$beng$$cEBLCP$$dGW5XE$$dYDX
001468461 049__ $$aISEA
001468461 050_4 $$aQA76.9.A25
001468461 08204 $$a005.8$$223/eng/20230613
001468461 1112_ $$aInternational Conference on Big Data and Security$$n(4th :$$d2022 :$$cXiamen, Xiamen Shi, China)
001468461 24510 $$aBig data and security :$$b4th International Conference, ICBDS 2022, Xiamen, China, December 8-12, 2022, Proceedings /$$cYuan Tian, Tinghuai Ma, Qingshan Jiang, Qi Liu, Muhammad Khurram Khan, editors.
001468461 2463_ $$aICBDS 2022
001468461 260__ $$aSingapore :$$bSpringer,$$c2023.
001468461 300__ $$a1 online resource (759 p.).
001468461 4901_ $$aCommunications in Computer and Information Science ;$$v1796
001468461 500__ $$a3 Methodology
001468461 500__ $$aIncludes author index.
001468461 5050_ $$aIntro -- Preface -- Organization -- Contents -- Big Data and New Method -- Data-Driven Energy Efficiency Evaluation and Energy Anomaly Detection of Multi-type Enterprises Based on Energy Consumption Big Data Mining -- 1 Introduction -- 2 Determination of the Optimal Number of Clusters -- 3 Energy Consumption Pattern Recognition Based on K-Means Clustering -- 4 Abnormal Analysis of Energy Consumption Data -- 4.1 LOF Algorithm -- 4.2 CEEMDAN Algorithm -- 5 Example Simulation and Analysis -- 5.1 Enterprise Energy Consumption Mode Division
001468461 5058_ $$a5.2 Abnormal Energy Consumption Detection for Enterprises -- 6 Conclusion -- References -- Research on Data-Driven AGC Instruction Execution Effect Recognition Method -- 1 Introduction -- 2 Independent Recurrent Neural Network -- 2.1 Recurrent Neural Network -- 2.2 Improvement of Deep Recurrent Neural Network -- 3 KPCA Preprocessing Process -- 4 Identification Process of AGC Instruction Execution Effect -- 5 Example Analysis -- 5.1 Basic Information of the Example -- 5.2 Comparative Analysis of Training Set and Validation Set -- 5.3 Comparative Analysis of DIndRNN Model and MLP Model
001468461 5058_ $$a5.4 Influence of Preprocessing Strategy on Model Prediction Accuracy -- 6 Conclusion -- References -- Research on Day-Ahead Scheduling Strategy of the Power System Includes Wind Power Plants and Photovoltaic Power Stations Based on Big Data Clustering and Filling -- 1 The Introduction -- 2 K-Means Clustering Method for Massive Power Grid Data -- 3 Missing Data Processing Strategy Based on Historical Data Assisted Scene Analysis -- 4 Construction of Power System Optimization Model Including Wind Power Plant and Optical Power Station -- 4.1 Doubly-Fed Asynchronous Fan
001468461 5058_ $$a4.2 Photovoltaic Power Array -- 4.3 The Objective Function -- 4.4 The Constraint -- 5 Example Analysis -- 5.1 The Example Described -- 5.2 Analysis of Optimization Results -- References -- Day-Ahead Scenario Generation Method for Renewable Energy Based on Historical Data Analysis -- 1 The Introduction -- 2 General Framework of Day-Ahead Scenario Generation Method for Renewable Energy -- 3 Cluster Analysis Based on Deep Embedding Clustering -- 3.1 Overall Framework of Deep Embedding Clustering Algorithm -- 3.2 Stacked Autoencoder -- 3.3 Initial Clustering of Low-Dimensional Features
001468461 5058_ $$a3.4 Joint Optimization -- 4 Day-Ahead Scenario Generation Based on C-DCGAN -- 4.1 Conditional Deep Convolution GAN -- 4.2 Building Models Based on C-DCGAN -- 4.3 Training of the C-DCGAN Model -- 5 Determination and Evaluation of the Day-Ahead Value -- 5.1 Determination of the Day-Ahead Prediction Value -- 5.2 Evaluation of the Day-Ahead Prediction Value -- 6 Example Analysis -- 6.1 Day-Ahead Scenario Generation -- 6.2 Method Comparison -- 7 Conclusion -- References -- A High-Frequency Stock Price Prediction Method Based on Mode Decomposition and Deep Learning -- 1 Introduction -- 2 Related Work
001468461 506__ $$aAccess limited to authorized users.
001468461 520__ $$aThis book constitutes the refereed proceedings of the 4th International Conference on Big Data and Security, ICBDS 2022, held in Xiamen, China, during December 812, 2022. The 51 full papers and 3 short papers included in this book were carefully reviewed and selected from 211 submissions. They were organized in topical sections as follows: answer set programming; big data and new method; intelligence and machine learning security; data technology and network security; sybersecurity and privacy; IoT security.
001468461 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed June 13, 2023).
001468461 650_0 $$aComputer security$$vCongresses.
001468461 650_0 $$aBig data$$vCongresses.
001468461 655_0 $$aElectronic books.
001468461 7001_ $$aTian, Yuan$$c(Professor of computer science)
001468461 7001_ $$aMa, Tinghuai.
001468461 7001_ $$aJiang, Qingshan.
001468461 7001_ $$aLiu, Qi$$c(Dean)
001468461 7001_ $$aKhan, Muhammad Khurram.
001468461 77608 $$iPrint version:$$aTian, Yuan$$tBig Data and Security$$dSingapore : Springer,c2023$$z9789819932993
001468461 830_0 $$aCommunications in computer and information science ;$$v1796.
001468461 852__ $$bebk
001468461 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-99-3300-6$$zOnline Access$$91397441.1
001468461 909CO $$ooai:library.usi.edu:1468461$$pGLOBAL_SET
001468461 980__ $$aBIB
001468461 980__ $$aEBOOK
001468461 982__ $$aEbook
001468461 983__ $$aOnline
001468461 994__ $$a92$$bISE