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Research on the value mining and behavior pattern analysis technology of low-voltage customers' electricity consumption

Research on the value mining and behavior pattern analysis t...

引用

2022 international conference on data Analytics, computing and Artificial intelligence, ICDACAI 2022

作者： Rongsheng, Li Xin, Gong Nan, Feng Xiaowei, Hao Na, Li Feng, Du State Grid Shanxi Electric Power Company Information & Communication Branch Shanxi Taiyuan030000 China State Grid Shanxi Electric Power Company Shanxi Taiyuan030000 China

ISBN: (数字)9781665454704

ISBN: (纸本)9781665454704

The current electricity consumption information collection system collects the full amount of electrical parameters for large users with high frequency, while only very little data is collected for low-voltage residential users, which leads to the scale and quality of current low-voltage residential users' electricity consumption data to be improved and makes it difficult to discover phenomena such as electricity theft. In this paper, the electric power big data mining technology is used to model the electricity consumption behavior of low-voltage residential users. By using data extraction, de-duplication, completion, cleaning, conversion and other processing techniques, the value of electricity consumption data can be explored and the quality of data can be improved, so that the patterns of electricity consumption behavior under normal or abnormal conditions can be analyzed. Based on actual residential customers' electricity consumption scenarios, the clustering analysis of low-voltage residential customers' electricity consumption behavior patterns is carried out, and an accurate and stable electricity consumption pattern recognition algorithm is established, which greatly improves the utilization rate of electricity consumption data. © 2022 IEEE.

关键词： data mining

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Context-based data Augmentation for Improved Ballet Pose Recognition 5

Context-based Data Augmentation for Improved Ballet Pose Rec...

引用

5th international conference on Artificial intelligence, big data, computing and data Communication Systems (ICABCD)

作者： Bowditch, Margaux Van der Haar, Dustin Univ Johannesburg Acad Comp Sci & Software Engn Johannesburg South Africa

ISBN: (数字)9781665484220

ISBN: (纸本)9781665484220

As computer vision technology continually advances and expands into various application fields, modern methods enable researchers to extract the most important and relevant key features from visual data. Human action recognition is one area that has gained much interest due to its potential in a variety of domains. Ballet, an art form filled with movements and poses, is a domain where the analysis of key features is particularly relevant. At a fundamental level, well-defined features used in a computer vision pipeline, generally provide more accurate predictions. This paper presents a feature engineering and pose analysis approach with OpenPose that produces pose feature templates from distance data. The calculated distance data is used to explore feature augmentation strategies for improved ballet pose recognition results. In addition to the OpenPose distance feature data, this paper investigates geometric and spatial feature approaches to determine optimal feature configurations for the ballet pose recognition task. A ballet dataset of eight distinct poses with multiple dancers was used for the study. The results demonstrate that the proposed approach provides a way to derive baseline pose skeletons from which statistical metrics are derived to aid in determining a valid ballet pose feature space. The augmentation of calculated OpenPose distance feature data yields improved results where the leading SVM classifier for the OpenPose feature space achieves an accuracy of 99.713%. The ensemble-based feature extraction approach which uses MobileNetV3 along with Augmented OpenPose features yielded an excellent Area Under the Curve (AUC) score of 99.999%. The proposed study, therefore, provides a valuable approach that results in state-of-the-art performance for the ballet pose recognition task.

关键词： Ballet OpenPose Feature Engineering data Augmentation SVM

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A new data process framework of industrial big data based on feature selection

A new data process framework of industrial big data based on...

引用

2022 international conference on Computer Network Security and Software Engineering, CNSSE 2022

作者： Xu, Fengjuan He, Haitong Gao, Jianlei Gong, Tianyu Li, Yaobing Li, Yun China Industrial Control Systems Cyber Emergency Response Team 35 Lugu Road Beijing100040 China China Agricultural University College of Information and Electrical Engineering 17 Qinghua East Road Beijing100083 China

ISBN: (纸本)9781510655980

The continuously updated and widely applied information technology and internet technology make the amount of data increase with an unimaginable speed. Subsequently, industries have also entered into the "Digital Times", which brings challenges and opportunities for industrialization. Through industrial big data government technology, we can continuously improve the level of industrial intelligence, the management efficiency and the production efficiency of enterprises, so as to improve the competitiveness of enterprises and the country. However, in the face of this large number of industrial big data with complex and diverse data characteristics, the application of traditional data processing methods in industrial big data government is not applicable, especially in the condition of valuable and scarce computing resources. A new proper data process framework, in this paper, is proposed for industrial data, whose principle is to reasonably select data features by utilizing a max-relevance min-redundancy (mrmr) algorithm that considering the relationship between data characteristics. The effect is verified on a series of industrial datasets. And the experiments results can effectively improve the utilization and analysis efficiency of data, reduce the difficulty of data processing, improve the data storage efficiency and reduce the data storage space. © 2022 SPIE.

关键词： big data

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Research on Intelligent Lithology Identification Method Based on Real-Time data of Drilling Wells 4

Research on Intelligent Lithology Identification Method Base...

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4th international conference on Robotics, Intelligent Control and Artificial intelligence, RICAI 2022

作者： Li, Tiantai Xiang, Ruiyi Jiao, Tong Yan, Xuedong College of Petroleum Engineering Xi'An Shi You University China

ISBN: (纸本)9781450398343

With the development of drilling technology, down-hole data monitoring systems such as follow-hole measurements have made the acquired data rich in variety and volume, and the acquisition of large amounts of data has made it feasible to predict formation properties using modern computer technology such as artificial intelligence. How to use big data and machine learning to reasonably analyze the formation properties and predict and identify the rock properties of the formation is also a hot research topic nowadays. In this paper, we model and analyze and predict the formation lithology using typical algorithms in machine learning for real-Time drilling data from the upper strata in the Huizhou area of the eastern South China Sea. The study first p reprocesses the real-Time drilling data using K-fold verification in order to improve the accuracy of lithology identification. This is followed by modeling using multiple algorithms in machine learning and constructing confusion matrices to evaluate the accuracy of various machine learning methods. Finally, feature engineering analysis is then performed on the real-Time drilling data to verify the impact of individual data on lithology identification. The results show that machine learning models can reasonably predict the identification of rock properties based on the support of big data, and the accuracy of lithology identification can be achieved by reasonable selection of modeling parameters. © 2022 ACM.

关键词： big data

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COVID-19 Public Opinion Information Analysis System 2

COVID-19 Public Opinion Information Analysis System

引用

2nd international conference on Artificial intelligence, Automation, and High-Performance computing, AIAHPC 2022

作者： Hu, Nan Liu, Hui Shuguang Big Data Academy Liaoning Institute of Science and Technology Benxi China

ISBN: (纸本)9781510657717

The arrival of COVID-19 has led to the emergence of a large amount of information, and inaccurate information will lead to group polarization and cognitive dissonance, so the management of public opinion information has become urgent. In this paper, the COVID-19 public opinion information analysis system selects the data sources on weibos as reference, and weibo hot topics, as the platform that users mainly focus on, are more authoritative and more credible compared to emerging media As the main platform for users' attention, Weibo hot topics are more authoritative and credible than emerging media. The selection of such a large platform can also ensure the openness and transparency of information. This paper focuses on a series of development processes such as acquisition and analysis of COVID-19 data and database design of COVID-19 public opinion information analysis system. The system not only realizes the basic functions of COVID-19 public opinion information analysis system, but also runs smoothly and interactively. © 2022 SPIE. All rights reserved.

关键词： COVID-19

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PRAD: Unsupervised KPI Anomaly Detection by Joint Prediction and Reconstruction of Multivariate Time Series 19

PRAD: Unsupervised KPI Anomaly Detection by Joint Prediction...

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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： Xiong, Zhiying Fan, Qilin Wang, Kai Li, Xiuhua Zhang, Xu Xiong, Qingyu Chongqing University School of Big Data and Software Engineering Chongqing China Chongqing University Chongqing Key Laboratory of Digital Cinema Art Theory and Technology Chongqing China Harbin Institute of Technology School of Computer Science and Technology Weihai China Harbin Institute of Technology Research Institute of Cyberspace Security Weihai China Haihe Laboratory of Information Technology Application Innovation Tianjin China University of Exeter Department of Computer Science Exeter United Kingdom

ISBN: (纸本)9798350346558

Detecting anomalies for key performance indicator (KPI) data is of paramount importance to ensure the quality and reliability of network services. However, building the anomaly detection system for KPI is challenging due to the scarcity of abnormal labels and highly dynamic or even unseen patterns. In this paper, we propose an unsupervised KPI anomaly detection framework named PRAD by jointly optimizing prediction-based and reconstruction-based modules. Specifically, PRAD employs two parallel graph attention networks (GATs) to learn metric-oriented and time-oriented relationships. A temporal convolutional attention network (TCAN) is used to capture complex long-term dependencies. Furthermore, to tackle the overfitting issue in the variational auto-encoder (VAE), PRAD designs a reconstruction-based module that combines VAE with a generative adversarial network (GAN). The experimental results on the two public datasets demonstrate that the proposed PRAD outperforms existing anomaly detection methods. © 2022 IEEE.

关键词： Anomaly detection

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Debiased Learning of Self-Labeled Twitter data for User Demographic Prediction

Debiased Learning of Self-Labeled Twitter Data for User Demo...

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2022 IEEE international conference on big data, big data 2022

作者： Wang, Zhen Cooley, Madison Zhang, Yang Lan, Chao Zhang, Ji Zhejiang Lab Research Institute of Artificial Intelligence Hangzhou China University of Utah School of Computing Salt Lake City United States University of Oklahoma School of Computer Science Norman United States University of Southern Queensland School of Mathematics Physics and Computing Toowoomba Australia

ISBN: (纸本)9781665480451

Labeling sufficient data for supervised learning remains an open challenge in social network analysis. An alternative is to collect self-labeled data, i.e. the data labeled by their owners. Emmery et al show that standard models can be trained and perform well on self-labeled data, suggesting the effectiveness of this approach. In this paper, we argue self-labeled data may not be representative of the population. Taking Twitter demographic prediction as an example, we show the popular FastText model standardly trained on self-labeled data does not generalize well on random testing samples. We then present a new learner DeFastText that aims to correct data bias using the kernel means matching technique. In experiment, we show it achieves lower generalization errors than FastText. This research raises an attention of the data bias problem when learning from self-labeled data in social network analysis. © 2022 IEEE.

关键词： Population statistics

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Exploring Enterprise Digital Transformation: The Perspective of Explainable Machine Learning

Exploring Enterprise Digital Transformation: The Perspective...

引用

international conference on Machine Learning, big data and Business intelligence (MLBDBI)

作者： Zhonghao Tan Peking University School of Software and Microelectronics Beijing China

ISBN: (数字)9798331541798

ISBN: (纸本)9798331505615

Digital transformation (DT) significantly enhances enterprise efficiency, innovation, and competitiveness through cutting-edge technologies such as artificial intelligence (AI), cloud computing, and big data. This study employs interpretable machine learning (ML) models to investigate the determinants of DT outcomes. The primary aim is to assess the impact of these technologies on operational efficiency, innovation, and market competitiveness. Using the 2011–2022 CSMAR Enterprise Digital Transformation database, we performed regression analyses and SHAP value assessments to elucidate the contributions of various factors. Our findings highlight the criticality of early adoption and ongoing advancement of digital processes. AI and cloud computing substantially improve digital outcomes, whereas broader innovation initiatives, though crucial, exhibit more variable effects. Theoretically, this study enriches DT frameworks by integrating interpretable ML insights. Practically, it provides actionable strategies for enterprises to achieve superior digital outcomes and maintain a competitive edge.

关键词： Industries Technological innovation Cloud computing databases Digital transformation Computational modeling Machine learning big data Regression analysis Internet of Things

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Application of Key Technologies of Intelligent Manufacturing in Metallurgical Industry Led by Artificial intelligence

Application of Key Technologies of Intelligent Manufacturing...

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2022 international conference on Cloud computing, big data and Internet of Things, 3CBIT 2022

作者： Yan, Feifei Liu, Jixuan Yan, Xuefeng Wang, Guoping Xi'an Jiaotong University City College Xi'an China China National Heavy Machinery Research Institute Co. Ltd. Xi'an China

ISBN: (纸本)9781665492256

Intelligent manufacturing, as a product of the deep integration of manufacturing and information technology, is the core technology of the new round of industrial revolution. The new generation of artificial intelligence presents new features such as deep learning, cross-border integration, human-machine collaboration and group intelligence, which provides humans with a new way of thinking to understand complex systems and a new technology to transform nature and society. The deep integration of new generation Al technology and advanced manufacturing technology has formed a new generation of intelligent manufacturing technology, which has become the core technology of the new round of industrial revolution and is the core driver of the fourth industrial revolution. However, the metallurgical industry is still in the key stage of transformation of the first generation of intelligent manufacturing (i.e. digital manufacturing), and the application of new generation of artificial intelligence technology is still in the primary stage. In order to catch up with the wave of the new round of technological revolution, it is urgent to summarize and analyze the key technologies of new generation of intelligent manufacturing with the industry characteristics of metallurgical industry. This paper analyzes the current development status of metallurgical industry, and analyzes and explores the application of new generation of key technologies of intelligent manufacturing such as intelligent machine tools, intelligent inspection technology, digital twin and 3D printing technology in metallurgical industry, which has certain practical significance for the transformation and upgrading of metallurgical industry for intelligent transformation, and can be used as a reference for relevant personnel. © 2022 IEEE.

关键词： Deep learning

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Smart Hospital Privacy Protection System Based On Cloud computing Iot 4

Smart Hospital Privacy Protection System Based On Cloud Comp...

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4th international conference on Machine Learning and big data Analytics for IoT Security and Privacy, SPIoT 2023

作者： Li, Sen Luo, Dan Liu, Xin Luo, Rong School of Information Network Security Xinjiang University of Political Science and Law Xinjiang Uygur Autonomous Region Tumshuk City843900 China College of Intelligence and Computing Tianjin Ren'ai University Tianjin301636 China Changji University Xinjiang Changji831100 China

The swift evolution of information technology is catalyzing a transformative fusion between cloud-integrated Internet of Things (IoT) and smart healthcare, poised to usher in revolutionary transformations within the medical sector. Through iot technology, various medical devices, sensors, and monitoring systems can connect and exchange data in real time, enabling remote monitoring, intelligent analysis, and personalized medicine. The confluence of the expanding Internet of Things (IoT) and the blossoming smart healthcare sector is set to streamline medical resource allocation, uplift healthcare service quality, and markedly ameliorate patients' experiences in healthcare settings. At the same time, it also brings some challenges, such as data privacy protection, standardization and security issues, which need continuous scientific and technological innovation and policy support to solve. In this paper, the row index obfuscation Bloom filter is designed in the scenario of smart healthcare to protect the privacy of data. Compared with other schemes, the proposed scheme has better privacy protection and better performance, and the proposed scheme is secure. In a word, the combination of iot and smart healthcare is promoting the digital transformation and innovative development of the medical industry, and providing more intelligent and convenient medical services for people's health. © 2024 The Authors.

关键词： Access Control Internet Of Things Privacy Preserving Smart Hospital

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