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Dynamic Impedance Modeling and Stability Analysis of Grid-Forming and Grid-Following Converters 8

Dynamic Impedance Modeling and Stability Analysis of Grid-Fo...

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8th IEEE international conference on Energy internet, ICEI 2024

作者： Cheng, Jifeng Zhang, Tingqi Jia, Qi Li, Xinwei Zhang, Xiaoheng Wang, Rui Electric Power Research Institute State Grid Liaoning Electric Power Co. Ltd. Shenyang China Northeastern University College of Information Science and Engineering Shenyang China

ISBN: (纸本)9798331523558

With the increasing proportion of renewable energy and power electronic equipment in power systems, the problems of broadband resonance and harmonic stability caused by the dynamic interaction between grid-connected inverters and the power grid become increasingly prominent. The dq impedance method based on a small signal model in the frequency domain in a synchronous rotating coordinate system is an effective tool for studying broadband harmonic resonance in a three-phase inverter grid-connected system. Given the lack of physical significance of the frequency-domain stability criterion and its derivative methods for the multi-input-multi-output system, In this paper, the dq impedance model of the grid-connected inverter is applied to small signal modeling and equivalent circuit construction of the grid-connected system, dynamic impedance modeling and stability analysis are carried out. Stability tests were carried out on the Simulink simulation to verify the validity of the stability analysis method. © 2024 IEEE.

关键词： impedance modeling small signal model stability analysis

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Proceedings of 2024 international conference on Intelligent Perception and Pattern Recognition, IPPR 2024

Proceedings of 2024 International Conference on Intelligent ...

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2024 international conference on Intelligent Perception and Pattern Recognition, IPPR 2024

ISBN: (纸本)9798400707315

The proceedings contain 21 papers. The topics discussed include: design and implementation of virtual reality emotion induction system;LSD: large-window semantic-aware discriminator for image super-resolution reconstruction;a novel telecommunication network service design method: hierarchical and object-oriented;the application of quantum cloud code IoT marking technology in the whole process of power equipment testing;research on financial forecasting systems based on artificial intelligence and blockchain;ground rotation object detection for UAV patrols using a rotated proposal generation network;iris template protection method based on dual mapping double bloom filter;signal extraction and damage identification of Derrick steel structure under noise background;intersection recognition integrating image and text information;and economic benefit analysis of mobile energy storage based on improved genetic algorithm.

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Optimization of RFID Attack Detection using a Received signal Strength Indication Comparison Method 28th

Optimization of RFID Attack Detection using a Received Signa...

引用

28th international conference on Knowledge based and Intelligent information and Engineering systems, KES 2024

作者： Lemieszewski, Lukasz Baraniecki, Jakub Prochacki, Szymon Department of Technology The Jacob of Paradies University Gorzów Wielkopolski66-400 Poland

The growing popularity of IoT (internet of Things) devices and the ever-increasing multiplicity of their applications ranging from security issues of automated locking systems in homes or hotels to contactless payments. This poses more and more challenges for RFID (Radio-Frequency Identification) users to raise the level of security. Problems of RFID-based devices often involve limited access to computing resources or persistent memory, which is associated with the low cost of the devices and their wide range of applications. The subject of this research is the comparative analysis of the power of communication signals between reader and transponder and reader and attacker. Measurement results were quantified, and the signal was compared for two types of modulations: ASK (Amplitude-Shift Keying) and PSK (Phase-Shift Keying), presenting their readings in the form of contrast histograms based on Fourier transforms. based on the readings obtained, a security method was proposed based on an algorithm that allows 100% detection of attempted attacks on RFID devices based on the signal power emitted by the transponder to the reader. The conditions under which the effectiveness of the method is possible were also determined based on experiments for IoT devices. © 2024 The Authors.

关键词： Radio frequency identification (RFID)

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Shallow Feature based image-Text Retrieval Using Kernel Extreme Learning Machine

Shallow Feature Based Image-Text Retrieval Using Kernel Extr...

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2024 IEEE international conference on signal Processing, Information, Communication and systems, SPICSCON 2024

作者： Ali, Lasker Ershad Mamun-Al-Imran, G.M. Islam, Md Zahidul Rahman, Md Azizur Mukherjee, Arnab Mathematics Discipline Khulna University Khulna9208 Bangladesh Department of Electronics and Communication Engineering School of Engineering Design and Built Environment Western Sydney University NSW2751 Australia International University of Business Agriculture and Technology Dhaka1230 Bangladesh

ISBN: (纸本)9798331510213

image-text retrieval has become more popular day-by-day due to its huge application on Facebook, YouTube, Google, LinkedIn, Twitter, etc. It is a cross-modal retrieval system considered a multi-class problem, which presents a challenging task in computer vision. Finding an appropriate combination of feature extraction and classification models is necessary to solve this multi-class problem effectively. This paper proposes a shallow feature-based cross-modal information retrieval system. Specifically, the scale-invariant feature transform (SIFT) is utilized as a shallow feature descriptor and the kernel extreme learning machine (KELM) is adopted as the classification model. Semantic matching (SM), correlation matching (CM), and semantic correlation matching (SCM) are applied in the matching strategy. The publicly available Wiki database is used for model learning. The mean average precision (mAP) is calculated as a statistical measure for each matching strategy, utilizing various distance metrics. The experimental results demonstrate that KELM achieves the highest mAP for SM, using a centered correlation measure, outperforming other matching strategies and statistical measures. Additionally, the results reveal that the SIFT feature-based KELM combination performs better than other competitive shallow feature-based approaches. © 2024 IEEE.

关键词： Adversarial machine learning

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Efficient MLP-Aided CNN Network with Local-global feature fusion for Hyperspectral image Classification

Efficient MLP-Aided CNN Network with Local-global feature fu...

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2024 international conference on image, signal Processing, and Pattern Recognition, ISPP 2024

作者： Du, Fei Li, Meng Li, Ju An, Xiaoxin Xu, Xinan Gao, Cuiping Han, Guodong College of Big Data and Internet Shenzhen Technology University Guangdong ShenZhen518000 China College of Grassland and Environment Key Laboratory of Grassland Resources of MOE Inner Mongolia Agricultural University Inner Mongolia Hohhot010011 China

ISBN: (纸本)9781510680425

The utilization of long-range spatial features has significantly enhanced hyperspectral image (HSI) classification performance. However, capturing global spatial relationships among pixels comes with a high computational expense. Given that super-pixels are created adaptively from the content within hyperspectral images, leveraging them for global spatial information extraction has proven to be an efficient strategy. Nevertheless, some methodologies at the super-pixel level, like Graph Convolutional Networks (GCN), are burdened by numerous parameters and substantial computational complexity. In order to address these challenges, this paper introduces an Efficient MLP-Aided CNN Network with Local-global Feature Fusion for Hyperspectral image Classification (EMACN). EMACN adopts a dual-branch structure, utilizing a super-pixel-based Multilayer Perceptron (MLP) for global feature extraction with a pixel-based Convolutional Neural Network (CNN) for local feature extraction, thereby achieving an effective balance between computational efficiency and classification performance. Compared to the quadratic complexity seen in other super-pixel level deep learning structures such as GCNs, the computational complexity of our super-pixel-based MLP approach is linear with respect to both the number and dimensionality of super-pixels. Furthermore, we enhance our model by integrating a spectral transformation subnet that utilizes group convolution instead of the traditional 1x1 convolution for reducing the dimensionality of hyperspectral images. Additionally, our model incorporates a pixel-based CNN module that employs 2D depthwise separable convolutions, optimizing computational efficiency. This design allows our network to achieve an optimal balance between its representation capabilities and computational expenses. Extensive testing across three datasets confirms the superior performance of our proposed network. © 2024 SPIE.

关键词： Hyperspectral imaging

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Point Cloud based In-vehicle Occupancy Detection Method by Using 77GHz mmWave Radar 2

Point Cloud Based In-vehicle Occupancy Detection Method by U...

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2nd IEEE international conference on signal, Information and Data Processing, ICSIDP 2024

作者： Cao, Xinda Liu, Jiangang Wu, Yulin Shao, Fengzhi Wang, Yubo Cui, Guolong Quzhou China University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu China

ISBN: (纸本)9798331515669

In recent years, with the introduction and development of vehicle-to-everything (V2X) and child presence detection (CPD), there's an increasing demand for in-vehicle perception systems. Millimeter-wave (mmWave) radar has become one of the mainstream sensors in this field for high accuracy, small size, and low power consumption. In this paper, we develop a real-time in-vehicle occupancy detection method based on 77GHz mmWave radar. Firstly, a pre-processing framework based on dual-path detection is used to obtain more point clouds from weak targets. Then density-based spatial clustering of applications with noise (DBSCAN) is applied to classify the occupants' point clouds. Finally, we propose a novel probabilistic population-assisted occupancy detection algorithm based on the long and short-term feedback results, which can suppress missed detection and false alarms well. Our algorithm has been successfully deployed onto the radar board and achieves an average accuracy rate of 99.10% under various complex scene experiments. © 2024 IEEE.

关键词： image segmentation

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A wandb based corrosion detection of metals through live video analytics 2

A wandb based corrosion detection of metals through live vid...

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2nd IEEE international conference on IoT, Communication and Automation technology, ICICAT 2024

作者： John Deva Prasanna, D.S. Sai Neeraj, Kuruvella Jaya Krishna, G. SRM Institute of Science and Technology Faculty of Engineering and Technology Department of DSBS India SRM Institute of Science and Technology Department of DSBS India

ISBN: (纸本)9798350368109

Detection of corrosion in moving objects like ships is challenging due to the dynamic nature of the input image. Existing machine learning techniques are suitable for static images and the algorithms suffer in performance when is a live video. In this paper, image processing for detecting corrosion using YOLOv8 which more suitable for processing live videos as speed and accuracy is better. This makes YOLO v8 for corrosion detection in live videos. In addition, Weights and Biases (W&B). is used in the algorithm as it is pivotal in establishing the connections between neurons and biases helps in circumventing flexible inputs. By combining YOLOv8's and W&B approach the accuracy and efficiency of corrosion detection systems is improved. This can ultimately assist in better maintenance and preservation of essential infrastructure resources. © 2024 IEEE.

关键词： Video signal processing

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UMAD: University of Macau Anomaly Detection Benchmark Dataset

UMAD: University of Macau Anomaly Detection Benchmark Datase...

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2024 international conference on Intelligent Robots and systems

作者： Li, Dong Chen, Lineng Xu, Cheng-Zhong Ko, Hui Univ Macau Fac Sci & Technol State Key Lab Internet Things Smart City SKL IOTS Macau Peoples R China Guangxi Normal Univ Key Lab Educ Blockchain & Intelligent Technol Minist Educ Guilin Peoples R China

ISBN: (纸本)9798350377712;9798350377705

Anomaly detection is critical in surveillance systems and patrol robots by identifying anomalous regions in images for early warning. Depending on whether reference data are utilized, anomaly detection can be categorized into anomaly detection with reference and anomaly detection without reference. Currently, anomaly detection without reference, which is closely related to out-of-distribution (OoD) object detection, struggles with learning anomalous patterns due to the difficulty of collecting sufficiently large and diverse anomaly datasets with the inherent rarity and novelty of anomalies. Alternatively, anomaly detection with reference employs the scheme of change detection to identify anomalies by comparing semantic changes between a reference image and a query one. However, there are very few ADr works due to the scarcity of public datasets in this domain. In this paper, we aim to address this gap by introducing the UMAD Benchmark Dataset. To our best knowledge, this is the first benchmark dataset designed specifically for anomaly detection with reference in robotic patrolling scenarios, e.g., where an autonomous robot is employed to detect anomalous objects by comparing a reference and a query video sequences. The reference sequences can be taken by the robot along a specified route when there are no anomalous objects in the scene. The query sequences are captured online by the robot when it is patrolling in the same scene following the same route. Our benchmark dataset is elaborated such that each query image can find a corresponding reference based on accurate robot localization along the same route in the pre-built 3D map, with which the reference and query images can be geometrically aligned using adaptive warping. Besides the proposed benchmark dataset, we evaluate the baseline models of ADr on this dataset. We hope this benchmark dataset will facilitate the advancement of ADr methods in the future. Our UMAD benchmark dataset will be publicly accessible at

关键词： Robots

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Enhancing RF Fingerprinting for Indoor Positioning systems Using Data Augmentation

Enhancing RF Fingerprinting for Indoor Positioning Systems U...

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2024 IEEE international conference on Consumer Electronics, ICCE 2024

作者： Junoh, Suhardi Azliy Jamil, Shawana Pyun, Jae-Young Chosun University Department of Information and Communication Engineering Gwangju Korea Republic of

ISBN: (纸本)9798350324136

Indoor Positioning systems (IPS) have recently emerged as a crucial technology in the internet of Things (IoT), with widespread applications in smart cities and homes. Radio frequency-based fingerprinting, enabling location estimation through signal observations, requires manual surveys for constructing location maps. This process involves annotating radio signatures with corresponding locations, rendering it time-consuming and labor-intensive. To address this challenge, our paper proposes a data augmentation method that leverages a conditional generative adversarial network with LSTM and CNN. This approach effectively captures patterns in the training data, generating synthetic data that aligns with the distribution. Experiments in a real scenario demonstrate an average localization error of 1.966 and 1.218 m for Wi-Fi and Bluetooth low energy (BLE), surpassing traditional fingerprinting and comparable to the baseline data augmentation methods. © 2024 IEEE.

关键词： Bluetooth low energy (BLE) data augmentation fingerprinting localization Generative adversarial network (GAN) internet of Things Wi-Fi

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Artificial Intelligence based Smart Guard for Home Automation 2

Artificial Intelligence based Smart Guard for Home Automatio...

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2nd international conference on Sustainable Computing and Smart systems (ICSCSS)

作者： Hussain, G. K. Jakir Ahamed, Z. Akkil Kumar, P. Arun Kumar, M. S. Hemanth Abishek, S. Jeevan KPR Inst Engn & Technol Dept Elect & Commun Engn Coimbatore 641407 Tamil Nadu India

ISBN: (纸本)9798350391558;9798350379990

For security reasons, this system utilized Raspberry Pi to create a facial recognition with door lock and unlock system. Two of the most significant universal rights are privacy and security. There is a great deal of research being done to guarantee security in our everyday lives through technology. Among these, one well-known and widely used technique is facial recognition. With the use of the internet of Things (IoT), faces may be recognized and identified in photos using this technology, making it even more accurate and valuable. This study intends to develop a smart door that authenticates users and secures the gateway based on face recognition and IoT. This system has utilized both Eigenfaces approach and the Viola-Jones method to identify faces and individuals in our proof of concept for such a smart security system. To guarantee the system's low cost and compact form, a Raspberry Pi has been employed as the microprocessor. When the processor receives a command, the door will automatically open for the recognized individual. Conversely, an email with a photo of the unknown will be uploaded and forwarded to the owner. From anywhere in the world, the owner can then grant or deny admission and sound an alarm with the aid of the GSM module.

关键词： Smart Guard image Recognition Short Message Service Alert Intruder Detection Residential Security Automatic / Manual Door Lock or Unlock

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