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检索条件"主题词=detection algorithm"

共 357 条记录，以下是181-190 订阅

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Principle of Arc Fault detection for Solid State Power Controller

Principle of Arc Fault Detection for Solid State Power Contr...

引用

58th IEEE Holm Conference on Electrical Contacts (Holm)

作者： Andrea, Jonathan Zirn, Olivier Bournat, Marc Esterline Power Syst F-57430 Sarralbe France

ISBN: (纸本)9781467307819

Solid state power controllers (SSPC) are semiconductor devices that control electrical power (voltage and/or current) supplied to a load. They perform supervisory and diagnostic functions in order to prevent overload conditions and short circuits. There are several basic types of solid state power controllers. SSPC controllers are designed to switch both AC and DC voltages. The SSPC is increasingly used in avionics systems primarily due to the emergence of the All Electrical Aircraft. Here we present the main differences between the SSPC and a relay associated to a circuit breaker. As the need for electric power has increased significantly and as the demand will be even higher in the future, then a need to protect the equipment against arc fault is becoming a major issue for safety in aviation. In this paper we present a microprocessor based SSPC which is able to detect the presence of an electrical arc in a circuit. We also describe a multi-algorithm detection architecture and we show how it is possible with this architecture to detect around 75 percent of arc faults occurring in a circuit. The results are obtained using a test bench described in this article.

关键词： SSPC Arc Fault detection algorithm

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Design and Implementation of an SNMP-Based Traffic Flooding Attack detection System

Design and Implementation of an SNMP-Based Traffic Flooding ...

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11th Asia-Pacific Network Operations and Management Symposium

作者： Park, Jun-Sang Kim, Myung-Sup Korea Univ Dept Comp & Informat Sci Seoul South Korea

ISBN: (纸本)9783540886228

Recently, as traffic flooding attacks such as DoS/DDoS and Internet Worm have posed devastating threats to network services, rapid detection and proper response mechanisms are the major concern for secure and reliable network services. However, most of the current Intrusion detection Systems (IDSs) focus on detail analysis of packet data, which results ill late detection and a high system burden to cope with high-speed network traffic. In this paper we propose an SNMP-based lightweight and fast detection algorithm for traffic flooding attacks, which minimizes the processing and network overhead of the detection system. minimizes the detection time, and provides high detection rate.

关键词： Traffic Flooding Attack DoS/DDoS SNMP MIB detection algorithm detection Time detection System

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Bone Fracture detection Based on Faster R-CNN with Bi-directional Feature Pyramid Module 7

Bone Fracture Detection Based on Faster R-CNN with Bi-direct...

引用

7th International Conference on Artificial Intelligence and Big Data (ICAIBD)

作者： Wu, Wenlong Qian, Yang Su, Zhihao Guangzhou Coll Appl Sci & Technol Comp Sci Coll Guangzhou Peoples R China La Consolac Univ Philippines Bulacan Grad Studies Dept City Of Malolos Philippines

ISBN: (纸本)9798350385113;9798350385106

With the widespread application of deep learning technology in the field of medical image processing, research on using convolutional neural networks for fracture detection has gradually increased. This paper proposes a Faster R-CNN network based on a bidirectional feature transfer mechanism (Bi-FPM), aiming to improve the accuracy of fracture detection. Through experimental comparison on the Kaggle Bone fracture dataset, this study explores the performance of different feature pyramid structures in the Faster R-CNN framework. Using ResNext-101 as the backbone, the proposed Bi-FPM method improves the accuracy by 5.8% in the fracture detection task compared to the baseline Faster R-CNN, and is higher than the traditional Feature Pyramid Network (FPN) Out 4.1%. Experimental results show that Bi-FPM's bidirectional feature transfer mechanism can effectively integrate deep and shallow features, improve the model's ability to identify fracture features and achieve better performance in fracture detection tasks. Through testing on the Kaggle Bone fracture dataset containing 350 test samples and 150 training samples, this paper confirms the effectiveness and superiority of the proposed method.

关键词： Deep learning bone fracture detection feature pyramid detection algorithm

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Line-shaped Non-precipitation Echo detection using Fuzzy Inference System

Line-shaped Non-precipitation Echo Detection using Fuzzy Inf...

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Joint World Congress of the International-Fuzzy-Systems-Association (IFSA) / Annual Meeting of the North-American-Fuzzy-Information-Processing-Society (NAFIPS)

作者： Lee, Hansoo Park, Ji Chul Kim, Jong Guen Kim, Sungshin Pusan Natl Univ Dept Elect Engn Pusan 609735 South Korea

ISBN: (纸本)9781479903481

In weather forecasting process, it is important to analyze weather radar data for classifying precipitation and non-precipitation echoes due to increase forecasting accuracy. There are lots of ongoing researches about non-precipitation echoes for detecting and removing them. In this paper, we present an algorithm to detect and remove line-shaped non-precipitation echo. The line-shaped echo can be divided into sun strobe echo that occurred by solar energy at sunrise or sunset and radial interference echo that occurred by electromagnetic interference wave. In order to detect and remove these two echoes, Mamdani fuzzy inference system is applied to combine expert's knowledge and characteristics. We tested the algorithm by applying the actual line-shaped echoes. And the result shows that the fuzzy inference system can distinguish not only sun strobe echo but also radial interference echo from actual radar data.

关键词： non-precipitation echo line-shaped echo Mamdani fuzzy inference system detection algorithm weather radar data

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detection of Respiratory Arrhythmia in Radar Based Heartbeat Estimation

Detection of Respiratory Arrhythmia in Radar Based Heartbeat...

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IEEE ITNC-USNC-URSI Radio Science Meeting / IEEE International Symposium on Antennas and Propagation (AP-S)

作者： Tchameni, Michelle R. Luecken, Volker Schroeder, Udo Diewald, Andreas R. Univ Appl Sci Lab Radar Technol & Opt Syst Trier Germany IEE SA Basics & Math Models Bissen Luxembourg

ISBN: (纸本)9798350359497;9789463968119

In this paper, an innovative non-contact method is used to monitor vital signs, specifically focusing on heartbeat estimation through radar signals. Our approach utilizes a cross-correlation method based on a template function, effectively isolating cardiac information from phase-extracted radar signals during breathing. By accurately identifying heartbeat peaks over time, our method showcases the potential of radar technology for real-time vital sign monitoring in healthcare applications. Experiments conducted with a 24 GHz FMCW radar system, compared with synchronized ECG data, highlight the efficiency of our approach. Beyond demonstrating non-contact cardiac monitoring, our method brings respiratory arrhythmia to the forefront, providing valuable insights into the intricate dynamics of cardiac and respiratory patterns. detection

关键词： FMCW radar time measurement vital signs monitoring cross correlation detection algorithm respiratory sinus arrhythmia.

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Ventricular tachycardia detection with the subcutaneous implantable cardioverter-defibrillator: Tracing the cause of an absent tracing

引用

HeartRhythm Case Reports 2020年第2期6卷 77-80页

作者： Mansour, Fadi Khairy, Paul Electrophysiology Service Centre Hospitalier de l'Université de Montréal (CHUM) Montreal Canada Electrophysiology Service Montreal Heart Institute Université de Montréal Montreal Canada

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Design of Automatic detection System of Ceramic Tile Flatness

Design of Automatic Detection System of Ceramic Tile Flatnes...

引用

2nd International Conference on Mechanics and Control Engineering (ICMCE 2013)

作者： Ma, YuLi Wang, Wei Qingdao Ocean Shipping Mariners Coll Qingdao Peoples R China Harbin Univ Sci & Technol Harbin Peoples R China

ISBN: (纸本)9783037859087

In order to achieve automatic detection of the tile flatness and automatic classification of product quality, the top control unit is touch screen, which can download parameters, display result information, control and debug device. Lower machine works as follows, when the tile is transferred to the test platform in the correct position, the laser displacement sensor collects 25 characteristic points information from moving the tile surface, via the A / D module,the information is converted into digital signals, according to specific flatness algorithm, PLC calculates flatness and achieves tile classification. Practice shows that the accuracy of tile flatness is 0.1mm, detection efficiency is 40 pieces per-minute. The system is suitable the quality control of ceramic tile product.

关键词： PLC automatic detect touch screen detection algorithm

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DCT-Tensor-Net for solar flares detection on IRIS data 7

DCT-Tensor-Net for solar flares detection on IRIS data

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7th European Workshop on Visual Information Processing (EUVIP)

作者： Ullmann, Denis Voloshynovskiy, Slava Kleint, Lucia Krucker, Sam Melchior, Martin Huwyler, Cedric Panos, Brandon UNIGE SIP CUI Geneva Switzerland FHNW I4DS Windisch Switzerland

ISBN: (纸本)9781538668979

Flares are an eruptive phenomenon observed on the sun, which are major protagonists in space weather and can cause adverse effects such as disruptions in communication, power grid failure and damage of satellites. Our method answers the importance of the time component in some scientific video observations, especially for flare detection and the study is based on NASA's Interface Region Imaging Spectrograph (IRIS) observations of the sun since 2013, which consists of a very asymmetrical and unlabeled big data. For detecting and analyzing flares in our IRIS solar video observation data, we created a discrete cosine transform tool DCT-Tensor-Net which uses an empirically handcrafted harmonic representation of our video data. This is one of the first tools for detecting flares based on IRIS images. Our method reduces the false detections of flares by taking into consideration their specific local spatial and temporal patterns.

关键词： big data applications computer aided analysis scientific computing data processing data analysis data preprocessing feature extraction image texture analysis detection algorithm discrete cosine transforms astrophysics

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Comprehensive evaluation and detection of partial discharge in WBG motor drive using DBSCAN based feature extraction

Comprehensive evaluation and detection of partial discharge ...

引用

2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023

作者： Choksi, Kushan Mirza, Abdul Basit Salehi Vala, Sama Luo, Fang Stony Brook University Spellman High Voltage Power Electronics Laboratory Stony BrookNY United States Stony Brook University Department of Electrical and Computer Engineering Stony BrookNY United States

ISBN: (纸本)9798350316445

The emergence of Wide Band Gap (WBG) technology has significantly enhanced the efficiency and power density of motor drives. As a result, WBG motor drives have widely been adopted in aircraft and transportation applications, particularly in More Electric Aircraft (MEA) systems. However, the reliability of the WBG high-power drive system is still a major concern. The reliability of medium and high-voltage motor drives sees an increased insulation degradation due to a more intense partial discharge (PD) exposure. Hence, PD condition monitoring, detection, and analysis are valuable for reliable operation and timely maintenance. This paper deals with PD detection at various voltage levels, dV/dt, and waveform shapes. This paper provides robust detection and analysis of PD events deriving the key relationship between PD probability and factors impacting PD events. This paper provides novel PD detection based on pattern recognition which is adaptable to varying voltage and frequency of supply. This algorithm can help the computation of charge per PD event as well as PD probability. © 2023 IEEE.

关键词： DBSCAN feature extraction detection algorithm Partial discharge PD detection setup

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Quickstop: A Markov optimal stopping approach for quickest misinformation detection

Quickstop: A Markov optimal stopping approach for quickest m...

引用

Proceedings of the ACM on Measurement and Analysis of Computing Systems

作者： Honghao, W.E.I. Kang, Xiaohan Wang, Weina Ying, Lei Arizona State University TempeAZ85287 United States University of Illinois at Urbana-Champaign Urbana Illinois61820 United States Carnegie Mellon University PittsburghPA15213 United States

This paper combines data-driven and model-driven methods for real-time misinformation detection. Our algorithm, named QuickStop, is an optimal stopping algorithm based on a probabilistic information spreading model obtained from labeled data. The algorithm consists of an offline machine learning algorithm for learning the probabilistic information spreading model and an online optimal stopping algorithm to detect misinformation. The online detection algorithm has both low computational and memory complexities. Our numerical evaluations with a real-world dataset show that QuickStop outperforms existing misinformation detection algorithms in terms of both accuracy and detection time (number of observations needed for detection). Our evaluations with synthetic data further show that QuickStop is robust to (offline) learning errors. © 2019 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Learning algorithms Machine learning Signal detection Social networking (online) detection algorithm Fake news Memory complexity Model driven method On line detection Optimal stopping Probabilistic information Quickest detection

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