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检索条件"任意字段=3rd International Conference on Signal and Data Processing, ICSDP 2023"

共 2953 条记录，以下是261-270 订阅

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Spark-KNN Parallel Pattern Recognition Technique for Substation Secondary Cable data 3

Spark-KNN Parallel Pattern Recognition Technique for Substat...

引用

3rd international conference on Electrical Engineering and Control Science, IC2ECS 2023

作者： Lu, Weizhou Huang, Haiying Luo, Huan Guangdong Power Grid Maoming Power Supply Bureau China

ISBN: (纸本)9798350382426

With the rapid development of smart grid, the substation secondary cable condition monitoring data is growing exponentially and gradually constitutes the secondary circuit condition monitoring big data. The traditional computing architecture can no longer meet the computing performance demand. Combining Spark big data processing technology and AliCloud E-MapReduce cloud computing platform, we propose a parallel pattern recognition method for substation secondary cable condition monitoring big data, aiming to improve the ability of the secondary cable online monitoring system to quickly batch analyze the alarm monitoring data that suddenly increase in a short period of time. Spark-KNN, a parallelized K-nearest neighbor classification algorithm based on Spark, is designed to realize parallel pattern recognition of massive secondary cable monitoring data. The experimental results show that the average performance of Spark-KNN is 3.17 times higher than that of Hadoop MapReduce implementation, which is more suitable for performing real-time processing tasks of secondary cable monitoring big data. © 2023 IEEE.

关键词： Online monitoring Secondary cable data Spark-KNN

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Enabling Rapid data Transfer in Medical IoT Systems with a High-Throughput Present Cipher Hardware Framework 3

Enabling Rapid Data Transfer in Medical IoT Systems with a H...

引用

3rd IEEE international conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Sohal, Jagmeet Centre for Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350343274

The proposed method aims to revolutionize data transfer speed and security in medical Internet of Things (IoT) systems by harnessing the high-throughput capabilities of the Present cipher through dedicated hardware. It seamlessly integrates three core algorithms: data packing, Present encryption, and optimized transmission. The steps involved in each algorithm, accompanied by their respective mathematical equations. data packing efficiently organizes medical data into packets, a vital step for subsequent processing and encryption. The Present encryption algorithm, a cornerstone of our approach, ensures secure data packets prior to transmission. Finally, optimized transmission guarantees efficient and swift data transfer within medical IoT systems. By incorporating these algorithms and equations, our method optimizes data transfer speed, energy efficiency, and data security, utilizing the high-throughput Present cipher hardware framework. © 2023 IEEE.

关键词： Energy efficiency

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Design and Implementation of IoT-Based Load Monitoring and Outage Management System 3

Design and Implementation of IoT-Based Load Monitoring and O...

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3rd international conference on Robotics, Electrical and signal processing Techniques, ICREST 2023

作者： Anwar, Ahmed Muntasir Hazari, Md. Rifat Mannan, Mohammad Abdul American International University-Bangladesh Department of Electrical and Electronic Engineering Bangladesh

ISBN: (纸本)9798350346435

An essential instrument for the operation of a power system is to monitor and analyze the data to find the fault and rectify it before the System collapses completely. This paper intents to utilize the idea to create a control system that will fulfill three objectives, monitoring of vital parameters controlling the power distribution, outage management by fault detection based on the variation of voltage, frequency, and current & protection of the circuit against any significant incidents by isolating the load from utility and flagging the information through feedback to the utility authority. The method used in this project can provide necessary safety from total system outages by adequately monitoring the instant data and historic data, managing the outage system by detecting faults, and cutting loads required to avoid a widespread blackout of a power system. Implementation of the proposed project can solve the problem of system blackout due to overload, under/over voltage, or under/over frequency. This developed system can supply necessary timestamped monitored data that can be accessed remotely and can also archive to create a proper load profile to ultimately help the modeling of Load Forecasting for a smooth and economic grid operation and can be used for developing the Smart Grid network. © 2023 IEEE.

关键词： Fault detection

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Research on English Text Keyword Extraction Algorithm Based on Big data Analysis 3

Research on English Text Keyword Extraction Algorithm Based ...

引用

3rd IEEE international conference on Electronic Communications, Internet of Things and Big data, ICEIB 2023

作者： Ma, Jingxia School of Western Languages and Cultures Harbin Normal University Harbin China

ISBN: (纸本)9798350333862

For effective keyword extraction of massive English Web texts, a Hadoop big data platform is proposed in this study. First, the Hadoop big data platform is reconfigured to support the natural language processing process. Secondly, the GATE method is used to implement word and sentence segmentation, personalized tagging, and annotation standardization on web English texts, thereby obtaining candidate keyword sets. Finally, the traditional TF-IDF algorithm is weighted using vocabulary localization and key factors to calculate the correlation between candidate keywords and the internal file. The results show that the keywords in the file can be used to identify file features and the proposed distributed keyword extraction method can quickly and accurately find keywords for web English text. © 2023 IEEE.

关键词： Big data

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Big data Application in Cancer Classification by Analysis of RNA-seq Gene Expression 3

Big Data Application in Cancer Classification by Analysis of...

引用

3rd IEEE international conference on Intelligent Technologies, CONIT 2023

作者： Amruth, A. Ramanan, R. Paul, Rhea Vishal, S. Saravanan, S. Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India Amrita School of Computing Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350338607

Computer Science domain has got massive developments in the past decade, especially in the field of Big data and Artificial Intelligence. Many industries are adopting these technologies to improve their products and services. Healthcare is one such industry which comes across voluminous data. Big data platform is used to process the data and subsequently AI models can be used for prediction or classification. This paper focuses on proposing a Spark based pipeline to reduce the time required to process the voluminous data. With the aid of this new pipeline, we classify the type of cancer through analysis of RNA-seq gene expression levels. The Spark-based pipeline is run on default standalone mode and on a cluster. The study recorded that the Spark-based pipeline run on cluster mode improved the performance approximately by 11 times (regarding processing costs) when compared to the performance of Python-based pipeline. © 2023 IEEE.

关键词： Diseases

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Research on Cloud Security Power Grid Model Simulation System Based on Computer Big data Technology 3

Research on Cloud Security Power Grid Model Simulation Syste...

引用

2023 IEEE 3rd international conference on data Science and Computer Application, ICDSCA 2023

作者： Xi, Wang Ming, Chen Shuang, Yang Dingwen, Zhang Yibin, Zhao Conghui, Xing China of Limited Company of Jilin Province Power Communication Company Changchun130021 China State Grid Jilin Electric Power Company Limited Changchun130021 China State Grid Info-Telecom Greate Power Science and Technology Co. Ltd Fuzhou350003 China

ISBN: (纸本)9798350341546

In this paper, a model of active distribution network safety monitoring system based on big data mining platform is established. This model combines multiple detection techniques and uses three-stage detection methods in the analysis equipment engine. A fuzzy encryption processing model of power flow information is proposed, which can improve the computing efficiency of static security analysis of power system under cloud computing environment on the premise of ensuring power flow information security. Through the simulation analysis of the fault diagnosis example, the results show that the proposed fault diagnosis algorithm is fast, accurate and effective. © 2023 IEEE.

关键词： Big data Cloud Security Computer Power Grid Model Simulation System

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Noise Reduction Method for Seismic data Based on Subspace Projection and Fast Fourier Convolution 3

Noise Reduction Method for Seismic Data Based on Subspace Pr...

引用

3rd international conference on Computer Vision and Pattern Analysis, ICCPA 2023

作者： Chen, Gongwen Su, Xuesong Yao, Qingyu Li, Siyuan School of Computer Science and Engineering Tianjin University of Technology TianJin China Technology Inspection Center of Shengli Oilfield SINOPEC Shandong Dongying China

ISBN: (纸本)9781510667563

The collected seismic data are highly contaminated due to random noise such as ambient noise, high and low frequency noise and instrument self-noise, which in turn affects the subsequent processing process. With the success of deep learning in image noise reduction, the application of deep learning to noise reduction of seismic data has also obtained better results than traditional noise processing methods. In this paper, we propose a noise reduction method for seismic data based on subspace projection and fast Fourier convolution (FFC). The method uses fast Fourier convolution to divide the channel into two branches, local and global, after downsampling, to update the feature map from two directions, and generates subspace vectors to separate seismic signal and noise according to the seismic data features to construct a seismic data noise reduction network. Experimental results show that the proposed method outperforms traditional seismic data noise reduction methods, such as wavelet transform, Fourier transform noise reduction, and classical deep learning methods FFDNet and NBNet, in terms of signal-to-noise ratio (SNR) measures. © 2023 SPIE.

关键词： Convolution

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Spatial data Analysis on On-Demand Cab Services using Spark 3

Spatial Data Analysis on On-Demand Cab Services using Spark

引用

3rd international conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Kokkiligadda, Manohar Raj Shaik, Fathimabi Likhitha, Poritigadda Velagapudi Ramakrishna Siddhartha Engineering College Department of Information Technology Vijayawada India Information Technology Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada India

ISBN: (纸本)9798350343632

Spatial data analysis is a technique used to analyze large amounts of spatial data generated by on-demand cab services such as Uber, Lyft, and Grab. This type of data includes information on the pickup and drop-off locations of riders, the trajectories of drivers, and the time when demand for cabs is highest. By analyzing this data, the on-demand cab services can gain insights on the customer demand, optimize driver allocation, improve pricing strategies, and enhance overall system efficiency. Spark is an open-source framework used for processing large-scale spatial data. It provides a set of APIs for processing and analyzing spatial data in distributed computing environments by using Apache Spark as its computing engine. This research explores the utilization of Spark, an open-source framework tailored for large-scale spatial data processing. Spark offers a comprehensive set of APIs designed for distributed computing environments, leveraging the power of Apache Spark as its underlying computational engine. By using Spark, on on-demand cab services can efficiently analyze large amounts of spatial data generated by their systems, enabling them to make data-driven decisions and improve the efficiency of their services. There by, we did analyze and visualize, handle the spatial data through spark with the help of graphs. © 2023 IEEE.

关键词： Spark Spatial data frame Spatial rdD

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Application of the Recursive Hybird Myriad Filter in Seismic data Denoising 3

Application of the Recursive Hybird Myriad Filter in Seismic...

引用

3rd international conference on Electronic Information Engineering and Computer Science, EIECS 2023

作者： Yue, Bibo Zhang, Han Li, Xiaolei Southwest Petroleum University School of Electrical Information Chengdu China

ISBN: (纸本)9798350316674

To solve the problem of the degradation of traditional seismic signal filter in non-Gaussian alpha-stable distributions, this paper introduces an improved Recursive Hybrid Myriad (RHMy) filter based on the Weighted Myriad filter(WMy). The RHMy filter combines the advantages of the WMy filter and the Weighted Mean filter, utilizes a fast algorithm to iteratively optimize the weight values. The RHMy filter was applied to denoising experiments on seismic signals and compared with the classical mean filter and the WMy filter. The results from theoretical model data testing and practical prestack seismic data applications demonstrate that the RHMy filter effectively suppresses impulse noise, increases the generalized signal-to-noise ratio(GSNR), and preserves useful information within the seismic signals. The performance of the introduced RHMy filter is significantly superior to the classical mean filter and the WMy filter. © 2023 IEEE.

关键词： alpha-stable detributions non-Gaussian recursive hybrid myriad seismic signals denoising

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Leveraging Neural Networks to Enhance Large-Scale data processing Performance 3

Leveraging Neural Networks to Enhance Large-Scale Data Proce...

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3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Pandey, Megha Vinay Kumar, S.B. Khurana, Jatin Kalpana, B. Pathak, Kishor R. Kumar, Mukesh Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India 1 Electronics and Communication Engineering Faculty of Engineering and Technology Karnataka Bangalore India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Vishwakarma Institute of Information Technology Department of Information Technology Pune India Vivekananda Global University Department of Mechanical Engineering Jaipur India

ISBN: (纸本)9798350319125

Recent advances in neural networks have enabled them to become powerful tools in data processing for tasks such as pattern recognition and classification. This has enabled neural networks to be applied to large-scale data processing problems, where they have been shown to perform very well. However, due to the large amount of data involved in such tasks, it is essential that the processing performance is also improved. This paper presents several methods to leverage available resources such as distributed computing and improved algorithmic techniques to improve the performance of large-scale data processing tasks using neural networks. Examples of such techniques are given for both supervised and unsupervised learning tasks, which can significantly enhance the performance of large-scale data processing applications. The results of several experiments are presented, demonstrating the effectiveness of the proposed techniques. The authors also provide insights for future work that could further enhance the capabilities of neural networks when applied to large-scale data processing applications. © 2023 IEEE.

关键词： Applications data Networks presented processing

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