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检索条件"任意字段=2023 IEEE 3rd International Conference on Data Science and Computer Application, ICDSCA 2023"

共 3016 条记录，以下是71-80 订阅

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An innovative multi-factor prediction algorithm construction and importance analysis model based on GBDT-LightGBM 3

An innovative multi-factor prediction algorithm construction...

引用

2023 ieee 3rd international conference on data science and computer application, icdsca 2023

作者： Wei, Dingkai Li, Yazi Li, Mengxiao Chang 'An University School of Materials Science and Engineering Xi'an China Hainan University School of Science Haikou China Shanghai University School of Management Shanghai China

ISBN: (纸本)9798350341546

With the rapid development of machine learning and artificial intelligence, the research of prediction models is becoming more and more popular. This paper aims to propose a general prediction model and analyze the importance of features. First, this paper builds the GBDT(Gradient boosting decision tree) and finally, the LightGBM (Light Gradient Boosting Machine) algorithm based on the Bagging Ensemble algorithm. MAPE and R2 were set as test indicators, and 31 groups of data from 1992 to 2022 were selected for training and learning. The results show that: (1) The GBDT-LightGBM model constructed in this paper has good robustness, with MAPE of 9.658 and R2 up to 0.899. (2) GBDT-LightGBM prediction model can better evaluate the influence of independent variables on dependent variables. This study provides an effective and general methodology for forecasting models. © 2023 ieee.

关键词： Bagging Feature importance GBDT LightGBM Prediction

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application of data Center Technology in Power data Resource Sharing 3

Application of Data Center Technology in Power Data Resource...

引用

2023 ieee 3rd international conference on data science and computer application, icdsca 2023

作者： Zhang, Longjun Zhuo, Yitao Wang, Tao Wang, Tianjun Chen, Jincheng State Grid Xinjiang Electric Power Co. Ltd. Information and Communication Company Urumqi830002 China State Grid Xinjiang Electric Power Co. Ltd. Urumqi830002 China State Grid Info-Telecom Great Power Science and Technology Co. Ltd Fuzhou350001 China

ISBN: (纸本)9798350341546

With the rapid development of power industry and Digital transformation, power enterprises have a large number of data resources, including power grid operation data, market transaction data, power equipment operation and maintenance data, etc. How to integrate and share these data resources, improve data utilization efficiency and business decision-making ability, has become an important issue faced by power enterprises. As an advanced data management and service platform, data center technology can effectively solve problems in power data sharing and bring enormous application value. This article conducts research and analysis on the application of data center technology in power data resource sharing, explores the key characteristics of data center technology, the requirements for power data sharing, and the application of data mining technology in power data sharing. Through experimental design and result analysis, the advantages and effects of data center technology in power data resource sharing are verified. © 2023 ieee.

关键词： data Access data Integration data Middleware Technology data Mining data Security Power data Sharing

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Incorporating text and emoticon features in sarcasm and irony detection: A deep learning-based approach 3

Incorporating text and emoticon features in sarcasm and iron...

引用

2023 ieee 3rd international conference on data science and computer application, icdsca 2023

作者： Ma, Xinjun Li, Tianning Jin, Zhida Chongqing University of Posts and Telecommunications College of Computer Science and Technology Chongqing China Shanghai Institute of Technology College of Computer Science and Information Engineering Shanghai China Northwest Normal University College of Computer Science and Engineering Lanzhou China

ISBN: (纸本)9798350341546

Emotions play a crucial role in social media interactions, and sarcasm and irony often convey complex emotions that can be challenging to detect accurately. This study focuses on automating the recognition of sarcasm and irony in Chinese through various computational methods using machine learning algorithms. This study utilizes both text and emoticon/emoji feature to improve detection accuracy. The study employs a combination of linguistic rules, word embeddings, and neural network models for detecting irony in Chinese microblog comments. The results highlight the advantages of deep learning techniques and pre-trained word embeddings in enhancing the accuracy of irony detection. Furthermore, the study demonstrates the viability of the proposed models by focusing on microblog posts related to social events. These contextualized experiments show promising results and pave the way for future studies to explore other domains and data sources. Overall, this study has made significant contributions to the field of irony recognition research and laid the groundwork for more complex models in the future, with practical implications for enhancing sentiment analysis in various fields. © 2023 ieee.

关键词： deep learning irony detection machine learning sarcasm detection sentiment analysis

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Research on Safety Monitoring and Defect Identification Method of Oil and Gas Pipeline Based on Faster R-CNN Technology 3

Research on Safety Monitoring and Defect Identification Meth...

引用

2023 ieee 3rd international conference on data science and computer application, icdsca 2023

作者： Xin, Hang Du, Xiaoran Sino-Pipeline International Company Limited Mansion HengYi No.5 Anding Road Chaoyang District Beijing China

ISBN: (纸本)9798350341546

For the problems of oil and gas pipelines in complex environments, such as difficulty in defect detection, high false alarm rate, and inaccurate positioning, this paper proposes a defect recognition algorithm based on Faster R-CNN. First, according to the Faster R-CNN model, RPN is established to obtain the confidence and coordinate system number of image features and effective anchor points. The information of the prediction box and target box is generated. Second, an ROI Pooling strategy is proposed to eliminate the size inconsistency between the prediction and target boxes. Third, concerning the defect characteristics of oil and gas pipelines, a classification regression model considering the influence of category and location is constructed to describe the defects more accurately. Finally, an oil and gas pipeline safety monitoring system based on the Faster R-CNN algorithm is built. Simulation and experiment prove the effectiveness of the proposed defect recognition algorithm. © 2023 ieee.

关键词： Defect recognition Faster R-CNN Oil and gas pipeline Region proposal network

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Research on the data Collection Method of Intelligent Vehicle Terminal for Production and Operation 3

Research on the Data Collection Method of Intelligent Vehicl...

引用

2023 ieee 3rd international conference on data science and computer application, icdsca 2023

作者： She, Mengyuan Gao, Yong Zhang, Xinzheng Tianjin Richsoft Electric Power Information Technology Co. Ltd Iov Operation Department Tianjin China

ISBN: (纸本)9798350341546

Vehicles are essential tools to support business travel and production operations. The development of new technologies and the demand for convenience are accompanied by an intelligent transportation transformation. To connect multiple types of terminal devices based on vehicle terminals, we can achieve multi terminal linkage, and support applications in various production scenarios, which is an urgent need for enterprises. This paper researches on the data collection method of intelligent vehicle terminal for production and operation. The collected data includes terminal management, communication data, vehicle fault remote diagnosis data, positioning data, Beidou short message, DMS data, ADAS data, video data, third-party equipment data access support, voice data, etc. Through the research of this paper, the vehicle terminal becomes the unified entrance of production data, as a edge computing device. It can support the access of multiple supporting sensing terminals. Through the research of IoT access technology, this paper achieves multi-source device data collection to support the development of production tasks. © 2023 ieee.

关键词： data Collection Edge Computing Perception terminal Vehicle Terminal

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Power System Stability Assessment System Based on Big data Analysis 3

Power System Stability Assessment System Based on Big Data A...

引用

2023 ieee 3rd international conference on data science and computer application, icdsca 2023

作者： Zhizi, Hou Rong, Tian Jiakang, Gu State Grid Shanghai Economic Research Institute Shanghai200000 China State Grid Shanghai Integrated Energy Services Co.Ltd Shanghai200023 China

ISBN: (纸本)9798350341546

This article studies a power system stability evaluation system based on big data analysis, which includes a data acquisition module, transmission module, big data analysis module, feedback module, evaluation module, and human-machine interaction module. The output end of the data acquisition module is connected to the input end of the transmission module through communication, while the output end of the transmission module is connected to the input end of the big data analysis module through communication, The output end of the big data analysis module is connected to the input end of the evaluation module, the output end of the evaluation module is connected to the input end of the human-machine interaction module, the output end of the evaluation module is also connected to the input end of the feedback module, and the output end of the feedback module is connected to the input end of the big data analysis module. This article can effectively evaluate the power system and facilitate staff to analyze the working conditions and maintenance of various power equipment. © 2023 ieee.

关键词： analysis big data data collection power system stability

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Research on Self-driving Vehicle Driving Status Monitoring and Trajectory Tracking Technology 3

Research on Self-driving Vehicle Driving Status Monitoring a...

引用

2023 ieee 3rd international conference on data science and computer application, icdsca 2023

作者： Zhai, Yuxin Gao, Yuchao Wang, Xin Shandong University of Science and Technology College of Transportation Qingdao China Shandong University of Science and Technology College of Electrical Engineering and Automation Qingdao China

ISBN: (纸本)9798350341546

Considering the research of driving state monitoring and trajectory tracking technology of autonomous vehicles in roads under low permeability conditions is an important technical means to alleviate traffic problems and ensure traffic safety, which is of great significance to improve the safety and robustness of autonomous driving. However, the traditional trajectory tracking algorithm has the problem of insufficient data accuracy and cannot effectively obtain the holographic traffic trajectory state. Accordingly, this paper proposes a forward vehicle state prediction algorithm based on the combination of the Auto-Regressice Moving Average (ARMA) model and Kalman filter to achieve accurate prediction of vehicle trajectory. At the same time, this paper also proposes a spatiotemporal fusion strategy based on the topological statistical distance of vehicle network data and roadside wide-area radar data and constructs a spatiotemporal correlation mechanism of the absolute position of vehicle GNSS and relative position information of millimeter wave radar detection to realize continuous and accurate location tracking service of vehicles and provide reliable data support for intelligent traffic control. © 2023 ieee.

关键词： Autopilot Autoregressive sliding average model Kalman filtering Topological statistics distance Track tracking algorithm

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Segment anything model for subgrade distress detection with GPR 3

Segment anything model for subgrade distress detection with ...

引用

2023 ieee 3rd international conference on data science and computer application, icdsca 2023

作者： Ning, Kangjie Zhou, Chunpeng Yu, Zhi Zhejiang University Zhejiang Provincial Key Laboratory of Service Robot College of Computer Science Zhejiang University School of Software Technology Hangzhou China Zhejiang University Zhejiang Provincial Key Laboratory of Service Robot College of Computer Science Zhejiang University School of Computer Science Hangzhou China

ISBN: (纸本)9798350341546

The detection of distresses with ground-penetrating radar (GPR) images is a highly challenging task due to their complex structure, blurred boundaries, and varying scales. The Segment Anything Model (SAM) has demonstrated impressive performance in natural image segmentation tasks with its zero-shot segmentation capability and high efficiency, which effectively reduces annotation time. However, the applicability of SAM for GPR image analysis remains till now. To this end, we construct a GPR image dataset comprising 395 samples with segmention, which are jointly confirmed and annotated by multiple domain experts. We comprehensively evaluate the performance of various mainstream deep learning image segmentation models and different SAM models on this dataset, and analyze the results in detail. Our experiments confirm that SAM's performance in detecting underground radar distresses is limited or even completely inadequate. These findings demonstrate that SAM's current zero-shot segmentation capability is insufficient for direct application in subgrade distress with GPR data. © 2023 ieee.

关键词： deep learning Foundation model ground-penetrating radar Intelligent traffic

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application of Carbon Emission Flow of Power Information Management System Based on Big data Intelligent Analysis 3

Application of Carbon Emission Flow of Power Information Man...

引用

2023 ieee 3rd international conference on data science and computer application, icdsca 2023

作者： Peng, Sui Luo, Ke Gu, Yanxun Tongd, Zhu Zhuhai Power Supply Bureau of Guangdong Power Grid Co. Ltd Zhuhai519000 China

ISBN: (纸本)9798350341546

With the increasing awareness of environmental protection, reducing carbon emissions globally has become an urgent task. As one of the industries with the highest carbon dioxide emissions, the power industry also urgently needs to adopt advanced technologies to reduce carbon emissions. This article aims to study a power information management system based on intelligent big data analysis to reduce carbon emissions and improve power efficiency. The system achieves rapid processing and accurate analysis of power information through big data analysis algorithms, and monitors, analyzes and optimizes carbon emissions based on real-time data. At the same time, the system can comprehensively manage the maintenance and operation of power equipment, further improving power efficiency and reducing carbon emissions. In conclusion, the power information management system based on intelligent big data analysis is an effective means to achieve automation and intelligence in the power system. It can improve power efficiency, reduce environmental pollution, and also promote the development of the power industry. © 2023 ieee.

关键词： Big data intelligent analysis Carbon emission flow system Power Information Management

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application of LSTM-CNN model to forecast the rise and fall of CSI 300 3

Application of LSTM-CNN model to forecast the rise and fall ...

引用

2023 ieee 3rd international conference on data science and computer application, icdsca 2023

作者： Cai, Jiaxue Zhang, Kaidi Jiang, Nan Harbin Institute of Technology Weihai Campus School of Economics and Management Weihai China

ISBN: (纸本)9798350341546

Stock price prediction has always been a focal issue in the field of quantitative finance. By analyzing historical data related to stock prices, researchers attempt to infer the patterns of stock price changes in order to achieve excess returns. However, due to the influence of multiple factors, traditional linear forecasting models often struggle to provide effective predictions. In recent years, non-linear predictive models using machine learning and deep learning methods have been widely used in quantitative investment. Compared to linear models, these models possess stronger predictive performance, learning ability, and adaptability. This paper conducts an empirical analysis from the perspectives of individual models (CNN model, LSTM model) and the combined CNN+LSTM model. It aims to predict the change of the stocks in the CSI 300 Index and compares the accuracy of their predictions. The results demonstrate that the combined CNN+LSTM model outperforms the individual models in predicting the directional movements of stocks in the CSI 300 Index. © 2023 ieee.

关键词： Asset pricing LSTM-CNN machine learning

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