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Research on Evaluation Method of Cable Line Operation State Based on Partial Discharge signal Characteristics 3

Research on Evaluation Method of Cable Line Operation State ...

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2022 3rd international conference on Electrical Technology and Automatic Control, ICETAC 2022

作者： Che, Renfei Zheng, Can State Grid Zibo Power Supply Company Shandong255000 China

To accurately evaluate the operation status of cable lines, an evaluation method based on the characteristics of partial discharge (PD) signals is proposed. In this method, the operation state of the cable line is divided into four levels, amount of PD and energy of PD in online monitoring data of the cable line are utilized, the development trend of cable insulation deterioration is considered, cable line state evaluation indexes are firstly established, then the state membership of each evaluation index is calculated by utilizing a membership function, proper operation operators are selected, Finally, the evaluation system of the operation state of the cable line based on the characteristics of partial discharge signals is constructed. The correctness and effectiveness of the proposed evaluation method are verified by setting three operating conditions of cable lines, which provides a decision-making reference for the maintenance of cable lines. © Published under licence by IOP Publishing Ltd.

关键词： Partial discharges

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Efficient AI Reasoning Model Based on FPGA 3

Efficient AI Reasoning Model Based on FPGA

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3rd international conference on Machine Learning and Big data Analytics for IoT Security and Privacy, SPIoT2023

作者： Ran, Junyi School of Big Data Chongqing Vocational College of Transportation Chongqing402247 China

The artificial intelligence inference model is currently the best mathematical model based on artificial neural networks. The intelligent reasoning mode based on artificial intelligence has a large construction scale. In a completed inference, many multiplication and addition operations need to be completed. To establish an effective artificial intelligence inference model, its computational complexity is dozens or even more times higher than traditional artificial intelligence algorithms. This article focused on the research on the isomerization acceleration of FPGA (Field Programmable Gate Array). This article was looking for a method that reduces both system changes and migration, while maximizing the flexibility of FPGA programmability. In order to build on this foundation, a fast interface for fast computation using FPGA was designed. The characteristic of this project was that it could combine the FPGA acceleration platform with ordinary computers or servers, and could effectively utilize traditional computer software and toolchains. This article compared the computational time consumption between the inference model and the CPU. In the scale calculation logic scenario of the inference model designed in this article, it could achieve the same computing power as the CPU when processing 70000 multiplication calculations. The experimental results indicated that the increase in CPU computing power was greater than that of the inference model. This indicated that the larger the amount of computational data, the more significant the acceleration effect of the inference model in this article would be. © 2023 Elsevier B.V.. All rights reserved.

关键词： Graphics processing unit

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Design and Implementation of Regulated Oxygenation Based Ventilation System with Feedback Mechanism

Design and Implementation of Regulated Oxygenation Based Ven...

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international conference on Robotics,Electrical and signal processing Techniques (ICREST)

作者： Rethwan Faiz Nuzat Nuary Alam Md. Abdullah Arafi Ayan Saha Mohammad Hasan Imam Electrical and Electronic Engineering American Internaational University-Bangladesh Dhaka Bangladesh

A multifunctional medical device for the aid of COVID affected patients are scarce. This paper proposes an automated medical device which is incorporated with a feedback mechanism and a GSM base emergency alarm system. The combined sensors in the prototype can acquire readings of a patient's temperature, heart rate, oxygen saturation (SpO2), respiratory rate (RR), and heart condition noninvasively and can send these vitals easily via SMS in real time. Based on the patient's SpO2 level and RR, the system can control the oxygen flow through a nasal canola with the aid of a servo motor mechanism. The system derives information from the sensors to operate automatically based on the degrading vitals of a patient. Due to its nature of user friendliness the protype can be operated without much prior medical knowledge.

关键词： Temperature sensors GSM Ventilators Medical devices signal processing algorithms Ventilation Sensors

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Classification and Storage Method of Medical Health Monitoring data Based on Bayesian Algorithm 3rd

Classification and Storage Method of Medical Health Monitori...

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3rd EAI international conference on IoT and Big data Technologies for Health Care, IotCARE 2022

作者： Li, Xiaofeng Chen, Zhongwei College of Information Engineering Guangxi University of Foreign Languages Nanning530222 China

ISBN: (纸本)9783031335440

In the storage of medical health monitoring data, the overall storage is chaotic and takes up more backup space. A classified storage method of medical health monitoring data based on Bayesian algorithm is designed. Collect and integrate medical health monitoring data. data collection is realized by relying on the unified data exchange platform in the big data platform. Each collection node uploads the required data by accessing the data exchange platform. data integration is mainly about data preprocessing of collected medical and health big data. Based on Bayesian algorithm, an integrated naive Bayesian algorithm based on knowledge transfer is designed to implement the classification and processing of medical health monitoring data. A hybrid storage model is designed to implement the classified storage of medical health monitoring data. Test the data classification and storage performance of the design method. The test results show that the method has less startup nodes, less computing power requirements as a whole, can realize the sharing of medical and health monitoring data, and the data classification time is relatively short as a whole, which proves that the method has a broad application space. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： data integration

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Performance Enhancement of Conventional Design of 4-Bit Carry Look-Ahead Adder

Performance Enhancement of Conventional Design of 4-Bit Carr...

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international conference on Robotics,Electrical and signal processing Techniques (ICREST)

作者： Upal Barua Joy Avishek Chakraborty Swagata Sen Arka Das Preyonti Biswas Afra Tasnim Dept. of EEE University of Science and Technology Chittagong Chattogram Bangladesh Dept. of EEE Premier University Chattogram Bangladesh

This research aims to increase performance parameters of conventional CMOS based 4-bit carry look-ahead (CLA) adder. CLA adder in conventional design fully utilize static CMOS (S-CMOS) logic for which its transistor count is quite high. Moreover, due to high input impedance, its delay is also high. To enhance performance and to reduce transistor count, (1) full swing gate diffusion input (GDI) based AND gates and (2) GDI based XOR gates have been utilized rather than using S-CMOS based AND and XOR gates. According to simulation conducted using Cadence tools, the proposed CLA adder design achieved noteworthy improvements in performance compared to the fully S-CMOS based existing design.

关键词： Surface impedance Buildings Logic gates signal processing Delays Transistors Impedance

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A Decision Feedback Model for Big data Analytics in Smart Grid 3rd

A Decision Feedback Model for Big Data Analytics in Smart Gr...

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3rd Biennial international conference on Future Learning Aspects of Mechanical Engineering, FLAME 2022

作者： Khatai, Swagat Sahoo, Swetaleena Rautaray, Siddharth Swarup Nanda, Sarita KIIT Deemed To Be University Bhubaneswar India

ISBN: (纸本)9789819913275

Smart grid provides new capabilities for the improvement of electrical grid control and operation. To enforce this capability smart meters, phasor measurement units (PMU), supervisory control and data acquisition (SCADA), and wide-area monitoring systems (WAMS) are installed in smart grid. These systems generate a huge amount of data every millisecond. The challenge of storing massive data and analyzing it in real time creates the inevitability of big data analytics in smart grid applications. Also, the integration of smart grid architecture with the existing analytics model is the major challenge. In this article, we have studied the sources of big data in smart grid PMU and have proposed an analytical model which is highly scalable and flexible for accommodating different types of data analytics and handling of massive data. The model is outclassed from the conventional data processing frameworks in the perspective of underlying architecture and control on iterative model execution for accurate detection of events or anomalies inside a smart grid and the classification of the event to take necessary decision. The proposed architecture is a big data-enabled and feedback-controlled model which seamlessly integrates data analytics and machine learning with the smart grid architecture. The execution of the model is controlled by a feedback loop considering the reference of event threshold which builds the novelty of the model. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： data Analytics

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Few-shot relation extraction based on multi-level feature metric learning 3

Few-shot relation extraction based on multi-level feature me...

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3rd international conference on Artificial Intelligence and Computer Engineering, ICAICE 2022

作者： Fang, YaXin Liu, ZhongLin Pan, Shuang Li, Qian North China Institute of Computing Technology Big data analysis department Beijing China

ISBN: (纸本)9781510663473

Relationship extraction is an important task in natural language processing and knowledge mapping. Traditional entity relationship extraction methods have achieved high accuracy in practical ***,when faced with the task of entity relationship extraction that is not easy to obtain large-scale supervision training data sets, traditional methods can not get good results. In this paper, a few-shot relation extraction method based on multi-level feature metric learning is proposed. This method takes the prototype network as the baseline network to generate a class prototype. Firstly, a multilevel feature extraction module is proposed. This module combines the multi-level features of the text with the multi-level attention mechanism, which can fully extract the features of the text. Secondly, a loss function based on label value and negative sample distance is proposed. This algorithm introduces an evaluation mechanism of negative sample distance on the basis of the prototype network, so that the model can adaptively allocate parameters and improve the clustering ability of small *** are conducted on FewRel1.0 which is a small sample relational data set. Experiment results show:compared with other models, our model can improve classification accuracy. © 2023 SPIE.

关键词： Extraction

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Risk Analysis of Infrastructure Asset-backed Securitization under Big data Environment 3

Risk Analysis of Infrastructure Asset-backed Securitization ...

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2023 3rd international conference on Big data, Artificial Intelligence and Risk Management, ICBAR 2023

作者： Zhang, Lijun Feng, Junwen Feng, Bo School of Economics and Management Nanjing University of Science and Technology Jiangsu Nanjing China School of Intellectual Property Nanjing University of Science and Technology Jiangsu Nanjing China Nanjing Audit University Jinshen College Jiangsu Nanjing China

ISBN: (纸本)9798400716478

Rating downgrade is an important signal of default, so this paper takes rating downgrade as an important basis for assessing the risk of asset-backed securitization (ABS) products. After analyzing the current situation of China's ABS market, we found that the rating downgrade concentrates in infrastructure enterprise asset-backed securitization (EABS) products. Therefore, this paper provides further risk analysis on infrastructure ABS products with big data mining and analysis technology. Firstly, we defined 9 independent variables. The dependent variable is a binary variable, which is recorded as 1 (Y=1) for downgrade_yes and 0 (Y=0) for downgrade_no. Secondly, we collected sample data from CNABS website and carry out necessary pre-process. Finally, we performed cross-tabulation analysis on the sample data and analyzed each independent variable's influence on rating downgrade with information gain (IG) method. It is found that spread, original-rating, year, region, and amount have a relatively large impact on rating downgrade, while industry, method, maturity and frequency have a smaller impact on rating downgrade. Based on the results, suggestions are put forward to effectively reduce rating downgrades. The issuer should optimize the securitization design, the investors should choose the appropriate products based on their preferences, and the regulators should strength supervision over rating downgraded products and reduce the default risk, which will help promote the sustainable development of infrastructure ABS. This study can also provide a reference for the promotion of PPP project asset-backed securitization (PPP-ABS) in the infrastructure field. © 2023 ACM.

关键词： Big data

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Efficient FPGA Implementation of a Convolutional Neural Network for Radar signal processing 3

Efficient FPGA Implementation of a Convolutional Neural Netw...

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IEEE 3rd international conference on Artificial Intelligence Circuits and Systems (AICAS)

作者： Zhang, Jingchi Huang, Yihao Yang, Huanrui Martinez, Michael Hickman, Granger Krolik, Jeffrey Li, Hai Duke Univ Elect & Comp Engn Dept Durham NC 27706 USA

ISBN: (纸本)9781665419130

Although neural networks, especially convolutional neural networks (CNNs), have been successfully applied to many domains, there have not found many radar applications mainly due to a paucity of available training data. Focusing on fixed-site radars, this work uses in-situ collected data to train a CNN classifier and suppress clutter components that allow targets to "hide in plain sight." This paper describes a software and hardware co-design approach for implementing a neural network to improve radar signal processing. At the algorithm level, we propose using the ResNet10 model structure and other optimizations trained using the angle-Doppler spectrum of returns at each range. The FPGA implementation is then carefully optimized to better tradeoff performance and energy efficiency. Experimental results show our approach achieves better performance than conventional methods and exceed the requirement by more than 2.5x. Meanwhile our energy consumption is much lower than other platforms like GPU. Our optimization methods can be applied to other CNN structures for efficiency improvement.

关键词： array processing CNN energy consumption FPGA radar signal processing

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Denoising and Super-Resolution of Multi-Source Remote Sensing Images Using Deep Learning Techniques 74

Denoising and Super-Resolution of Multi-Source Remote Sensin...

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74th international Astronautical Congress, IAC 2023

作者： Wang, Kaige Huang, Degang Ma, Jifeng Zhang, Hongjiang Du, Gang Wang, Zhaoqiu Zhao, Xiaoning 2050 Laboratory China Academy of Aerospace Science and Innovation 2 Taihe 3rd Street Daxing District Beijing China Department of Aerospace Engineering Beijing University of Posts and Telecommunications 10 Xitucheng Road Haidian District Beijing China

Artificial intelligence technology has been rapidly developing and widely used in the past decade as researchers continue to study AI theory and the growth of hardware computing power driven by Moore's Law. In the field of aerospace, AI technologies are emerging and playing an increasingly critical role in spacecraft design, manufacturing, launch, and applications. For instance, with the advantage of complex connected neural network structure and hardware parallelization data processing, AI technology has incomparable fast, convenient and accurate processing effect for Earth observation remote sensing images generated by spacecraft. The remote sensing images produced by spacecraft for Earth observation suffer from low signal-to-noise ratio and low spatial resolution due to the limitations of long observation distance, unstable Earth environment, pronounced cosmic radiation effect, sensor manufacturing process, etc. Nevertheless, the quality of remote sensing images directly affects the effectiveness of Earth observation tasks such as object detection and identification. Meanwhile, de-noising and super-resolution images on the ground side can reduce the in-orbit processing operations of satellites, lessen the demand for satellite storage, reduce the occupation of the sky and earth links, and enhance the reliability and real-time information preservation, processing and transmission. Therefore, image denoising and super-resolution technologies have become valuable research directions in the field of Earth observation. Traditional denoising and super-resolution algorithms have shortcomings, such as complex and challenging algorithm design, poor data quality enhancement, and extended algorithm processing time. At the same time, artificial intelligence technology can obtain powerful mapping ability and fitting ability by training neural networks to improve remote sensing image quality and enhance remote sensing image clarity more obviously. Thereby, it contributes to the

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