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检索条件"主题词=detection algorithms"

共 7403 条记录，以下是1191-1200 订阅

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CPS-Net: Segmentation Network Based on Feature Fusion and Siamese

CPS-Net: Segmentation Network Based on Feature Fusion and Si...

引用

Frontiers Technology of Information and Computer (ICFTIC), IEEE International Conference on

作者： Ke Shi John Xu Jian Mao School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798331541750

ISBN: (纸本)9798331541767

Traditional deep convolutional neural networks are weak in handling multi-scale issues in tooth CT image segmentation. Based on the evolution of siamese network vision and image cutting algorithms, we make progress when teeth are occluded or deformed. Thus, a siamese network image segmentation algorithm combined with feature fusion is proposed. Due to network degradation and gradient dispersion, different backbone networks are selected for distillation experiments for overly complex networks. The siamese model constructs a feature pyramid, improving the overall model's ability. The new method is better at handling multi-scale features of tooth deformation.

关键词： Degradation Image segmentation Adaptation models Fuses Deformation Teeth Feature extraction Dentistry detection algorithms Dispersion

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A Pick and Place Mechanism with Vision to Sort Objects of Different Colour and Size

A Pick and Place Mechanism with Vision to Sort Objects of Di...

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Communication, Computing and Industry 4.0 (C2I4), International Conference on

作者： Shweta J Sreeja Kochuvila Department of Electronics and Communication Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798350379884

ISBN: (纸本)9798350379891

The traditional pick and place mechanisms in manufacturing and automation often lack adaptability, precision, and cost-effectiveness, when handling objects of different sizes and colours. This paper describes an innovative pick and place system that employs vision sensors to enhance the size and colour detection capabilities. The proposed system utilises a pre-trained monocular depth detection model along with a custom-made size detection algorithm that can measure object's dimensions accurately without having to resort to stereo vision systems. For colour detection, we adopted HSV colour space to ensure strong operation even under different lighting scenarios. A smart phone camera is used as a vision sensor to test the performance of the system. It is demonstrated that the proposed system greatly increases the pick and place machine's efficiency and versatility, thereby making it more appropriate for today’s fast-changing industrial settings.

关键词： Deep learning Automation Accuracy Image color analysis Lighting Vision sensors Cameras Grippers detection algorithms Smart phones

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Bubble or Not: An Analysis of Ethereum ERC721 and ERC1155 Non-fungible Token Ecosystem

Bubble or Not: An Analysis of Ethereum ERC721 and ERC1155 No...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Yixiang Tan Zhiying Wu Jieli Liu Jiajing Wu Ting Chen Kaixin Lin School of Computer Science and Engineering Sun Yat-sen University Guangzhou China School of Software Engineering Sun Yat-sen University Zhuhai China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

The non-fungible token (NFT) is an emergent type of cryptocurrency that has garnered extensive attention since its inception. The uniqueness, indivisibility, and humanistic value of NFTs are the key characteristics that distinguish them from traditional tokens. The market capitalization of NFT reached 21.5 billion USD in 2021, almost 200 times that of all previous transactions. However, the subsequent rapid decline in NFT market fever in the second quarter of 2022 casts doubt on the ostensible boom in the NFT market. To date, there has been no comprehensive and systematic study of the NFT trade market or of the NFT bubble and hype phenomenon. To address this gap, we conduct an in-depth investigation of the entire Ethereum ERC721 and ERC1155 NFT ecosystems by dividing the NFT ecosystem participants into three categories: creators, transferors, and holders, to understand their manipulations of NFTs and infer their intentions. We examine the differences between NFT and ERC20 traders and then analyze the possible reasons behind these differences, leading us to gain insights into the varying degrees of market bubble associated with the two kinds of tokens. Through the construction and analysis of the NFT transfer graph (NTG), we uncover certain anomalous behaviors related to bubbles, along with quantifying the proportion of these anomalous behaviors, to reveal the degree of bubbles within the entire ecosystem.

关键词： Systematics Circuits and systems Ecosystems Feature extraction Cryptocurrency Nonfungible tokens detection algorithms

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Study of fast startup criterion for AC microgrid faults based on Hankel matrix singular value decomposition

Study of fast startup criterion for AC microgrid faults base...

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Energy, Power and Grid (ICEPG), International Conference on

作者： Yudong Fang Yutao Qiu Biyun Qi Juan Fang State Grid Zhejiang Electric Power Co. Ltd Hangzhou China Quzhou Electric Power Bureau of Zhejiang Electric Power Company Quzhou Zhejiang China

ISBN: (数字)9798350377798

ISBN: (纸本)9798350377804

New energy generation technologies such as photovoltaic and wind turbines are frequently integrated into AC microgrids as distributed power sources. This integration presents new challenges for fault detection and other aspects of microgrid operation. This study focuses on the rapid detection of faults in AC microgrids. The performance benefits of utilizing the wavelet algorithm and Hankel matrix singular value decomposition for detecting signal anomalies are examined. A fault start determination method based on Hankel matrix singular value decomposition is proposed to address the detection delay issue associated with conventional fault start determination methods. This approach enables the precise identification of the fault start moment.

关键词： Wavelet transforms Photovoltaic systems Fault diagnosis Fluctuations Microgrids Wind turbines Matrix decomposition Protection detection algorithms Singular value decomposition

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Generalized Likelyhood Ratio based detection on Cyber-Attacks

Generalized Likelyhood Ratio based Detection on Cyber-Attack...

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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Hao Wu Deyu Zhang dept. of Software Henan University of Engineering Zhengzhou China Institute for Automatic Control and Complex Systems University of Duisburg-Essen Duisburg Germany

ISBN: (纸本)9781665465373

In this paper, a generalized likelihood ratio based detection algorithm with a statistical method for threshold setting is proposed to detect cyber-attacks on a unified remote control architecture. Based on an encoder and a decoder embedded on plant side and remote controller side, respectively, not only additive interrupted signal (only effecting output or input signal), but also multiplicative attack signal could be detected easily. Finally, three numerical examples are provided to validate our proposed scheme.

关键词： Additives Automation Statistical analysis Switches Decoding detection algorithms Cyberattack

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Massive MIMO-Enabled Semi-Blind detection for Grant-Free Massive Connectivity

Massive MIMO-Enabled Semi-Blind Detection for Grant-Free Mas...

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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Malong Ke Zhen Gao Shufeng Tan Liang Fang Mengnan Jian Hanqing Xu Yajun Zhao School of Information and Electronics Beijing Institute of Technology Beijing China Advanced Research Institute of Multidisciplinary Science Beijing Institute of Technology Beijing China Beijing TASSON Technology Co. Ltd Beijing China State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen China ZTE Corporation Beijing China

This paper studies the reliable support for massive machine-type communications and proposes an efficient semi-blind detection scheme for grant-free massive connectivity. In the proposed scheme, each active device directly transmits a very short reference signal along with its payload data in the uplink, without any access scheduling in advance. At the base station (BS), we develop a successive interference cancellation (SIC)-based semi-blind detection algorithm to detect active devices and their payload data. Specifically, benefitting from the large spatial dimensionality of the BS antenna array, the bilinear generalized approximate message passing algorithm is employed for joint channel and signal estimation (JCSE). In particular, we introduce an a priori refining strategy to leverage the structured sparsity of the massive access channel matrix for improved JCSE performance. Moreover, the inserted reference signal is utilized to resolve the inherent phase and permutation ambiguities. Besides, the idea of SIC is adopted for further improved detection accuracy, where the cyclic redundancy check and soft pilot-based channel refining are incorporated to prevent error propagation. Numerical results demonstrate that the proposed semi-blind detection scheme outperforms the state-of-the-art training-based coherent detection scheme when the same number of physical resources are occupied.

关键词： Wireless communication Interference cancellation Message passing Refining Coherence Reliability detection algorithms

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Incipient Slip detection for Rectilinear Movements using the PapillArray Tactile Sensor

Incipient Slip Detection for Rectilinear Movements using the...

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IEEE Sensors

作者： Pablo Martinez Ulloa David Cordova Bulens Stephen J. Redmond Schoo of Eecric nd Eecronic Engineering Universiy Coege Dubin (UCD) IrendSchoo of Eecric nd Eecronic Engineering Universiy Coege Dubin (UCD) IrendSchoo of Eecric nd Eecronic Engineering Universiy Coege Dubin (UCD) Irend

ISBN: (数字)9781665484640

ISBN: (纸本)9781665484657

It is critical during dexterous manipulation to avoid dropping the object. It is believed that humans can rapidly detect object slip and subsequently correct the applied gripped forces to prevent losing the grasp. In this paper, we present, validate, and analyze an incipient slip detection method using force and displacement signals from the PapillArray tactile sensor, detecting slip on some of the sensors' pillars before gross slip occurs. Slips events were verified by external camera tracking and these verified slip events used to validate a slip detection algorithm that operates solely on the PapillArray signals. The proposed method was successful in identifying the instances of slip using PapillArray data (83% precision and 82% sensitivity) and in being able to detect incipient slip before gross slip for different velocities of lateral movements. Future work will test its effectiveness on rotational movements, and non-planar object surfaces.

关键词： Sensitivity Force Tactile sensors Cameras Sensors detection algorithms

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An application of XGBoost algorithm for online transaction fraud detection based on improved sailfish optimizer

An application of XGBoost algorithm for online transaction f...

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International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI)

作者： Hongwei Chen Lun Chen School of Computer Science Hubei University of Technology Wuhan China

Fraud detection is relevant in this context because of the large scale of online transactions, which conceal a small number of fraudulent *** learning (ML) fraud detection models have numerous hyperparameters of the eXtreme Gradient Boosting (XGBoost) algorithm, which are important for the performance impact of fraud detection models. In this paper, the propose an improved version based on Sailfish optimizer(SFO) applied to online transaction fraud detection to optimize the hyperparameters of the XGBoost algorithm. Firstly, the optimization-seeking and local-optimal-skipping capabilities of Sailfish optimizer are improved, and Improved Sailfish optimizer (ISFO) is compared with other classical optimization algorithms, and the results show that the algorithm has outstanding. The Improved Sailfish optimizer is then used to optimize the parameters of XGBoost (ISFO-XGBoost) and compared with several other machine learning classifiers such as: Decision Tree (DT), Random Forest (RF), XGBoost, and several optimization algorithms optimized for the XGBoost model. To validate the performance, a fraud dataset of online transactions is used for evaluation. The experimental results demonstrate that the proposed improved algorithm is highly promising in solving machine learning hyperparameter optimization challenges, and the ISFO-XGBoost algorithm achieves the best results.

关键词： Radio frequency Measurement Machine learning algorithms Hyperparameter optimization Fraud Classification algorithms detection algorithms

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detection of Attacks using Attention-based Conv-LSTM and Bi-LSTM in Industrial Internet of Things

Detection of Attacks using Attention-based Conv-LSTM and Bi-...

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Automation, Computing and Renewable Systems (ICACRS), International Conference on

作者： Bebin Josey T D.S. Misbha Department of Computer Science Nesamony Memorial Christian College Marthandam India Nesamony Memorial Christian College Marthandam India

ISBN: (纸本)9781665460859

This research work proposes an attack detection algorithm for the industrial internet of things (IIoT) which uses an attention-based Conv-LSTM and Bidirectional Long short-term memory (Bi-LSTM) network. The approach consists of two feature extraction modules namely attention-based Bi-LSTM feature extraction and Convolutional LSTM (Conv-LSTM) feature extraction algorithm that extracts the features on the same data. The Bi-LSTM further extracts two types of features where one type is extracted in the forward pass while the other is extracted in the backward pass. The Bi-LSTM and Conv-LSTM extracted features are fused and are trained/tested using the fully connected layer of the neural network to detect normal or abnormal data. The evaluation of the algorithm was performed using the metrics namely false negative rate, false alarm, detection rate, and accuracy with the IIoT dataset namely AWID, and CTU-13. The proposed approach provides an accuracy of 98.02% and 95.98% for the AWID, and CTU-13 datasets respectively which shows that the algorithm outperforms other recent IIoT attack detection algorithms.

关键词： Measurement Renewable energy sources Automation Neural networks Feature extraction Data mining detection algorithms

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Framework for Image Sensor Design Parameter Optimization for Pupil detection

Framework for Image Sensor Design Parameter Optimization for...

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International Conference on Systems and Informatics (ICSAI)

作者： Gernot Fiala Zhenyu Ye Christian Steger Institute of Technical Informatics Graz University of Technology Graz Austria OS R&D SYS SW ams-OSRAM AG Premstaetten Austria

ISBN: (纸本)9781665474498

Machine vision systems (MVS) use image sensors to process and analyze image data. Depending on the application, the image sensor parameters are configured differently. However, some parameters are fixed for a specific product generation or product line. One of these parameters is the pixel pitch, the distance from one physical pixel to another. In this work, we introduce a framework, which allows to optimize design parameters of image sensors for pupil detection. We compare 2 different image sensor models with different pixel designs and generate images with different bit depths and resolutions. An evaluation of the design parameters is done with the generated images and a pupil detection algorithm. Furthermore, an existing pupil detection dataset is extended.

关键词： Image sensors Image resolution Machine vision Pupils Informatics detection algorithms Optimization

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