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Modulation RecognitionTechnology based on Feature Extraction 4

Modulation RecognitionTechnology Based on Feature Extraction

引用

4th international conference on Electronic Information engineering and computer Technology, EIECT 2024

作者： Zhu, Liang Liu, Min Zhu, Xiaodi Dong, Yufei Zhao, Jie Systems Engineering Research Institute Shipbuilding Corporation Limited Beijing100094 China

ISBN: (纸本)9798331528850

This paper explores modulation recognition technology based on feature extraction, which is a key means of identifying different modulation types of signals in modern communication systems. By preprocessing and feature extraction of the received signal, this technology can effectively extract key information from complex signal environments and identify the modulation mode of the signal. The paper first introduces the background and significance of modulation recognition technology, and then elaborates on the methods and steps of feature extraction, including various feature extraction techniques such as time domain, frequency domain, and high-order statistics. Subsequently, the paper discussed the effectiveness of different features in modulation recognition and verified the effectiveness of the proposed method through experiments. Finally, the advantages and challenges of modulation recognition technology based on feature extraction were summarized. © 2024 ieee.

关键词： Signal modulation

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Realisation of a Low-Cost Three-Axis CNC for Processing Soft Materials

Realisation of a Low-Cost Three-Axis CNC for Processing Soft...

引用

2023 international conference on Electromechanical and Energy systems, SIELMEN 2023

作者： Palcu, Alexandru Lucache, Dumitru Dorin Gheorghe Asachi Technical University of Iasi Faculty of Electrical Engineering Iasi Romania

ISBN: (纸本)9798350315240

The large-scale use of CNC machine tools has become a necessity both in the industrial environment and in workshops and smaller businesses. The precision in processing, the productivity and the diversity of the work tasks offered by the numerical control are the main arguments. But the cost of professional equipment is high and for small workshops, where the desired performance from the machine is not extremely high, in-house building of a CNC represents a solution, especially when the personnel have knowledge in electrical engineering. The work presents the conception and realization of a CNC machine tool with numerical control for the processing of soft materials. The process has two stages, first a prototype was built and, then, the construction and design errors were removed to increase the machine tool performance. © 2023 ieee.

关键词： computer control systems

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Predicting Compute Node Unavailability in HPC: A Graph-based Machine Learning Approach

Predicting Compute Node Unavailability in HPC: A Graph-Based...

引用

2024 workshops of the international conference for High Performance Computing, Networking, Storage and Analysis, SC workshops 2024

作者： Krumpak, Roy Rozanec, Joze M. Molan, Martin Angelinelli, Matteo Bartolini, Andrea Jožef Stefan Institute Laboratory of Artificial Intelligence Ljubljana Slovenia University of Bologna DEI Department Bologna Italy CINECA HPC Department Bologna Italy

ISBN: (纸本)9798350355543

As high-performance computing (HPC) systems advance towards Exascale computing, their size and complexity increase, introducing new maintenance challenges. Modern HPC systems feature data monitoring infrastructures that provide insights into the system's state. This data can be leveraged to train machine learning models to anticipate anomalies that require compute nodes to undergo maintenance procedures. This paper presents a novel approach to predicting such anomalies by creating a graph per measurement that encodes current and past sensor readings and information related to the compute node sensors. The experiments were performed with data collected from Marconi 100, a tier-0 production supercomputer at CINECA in Bologna, Italy. Our results show that the machine learning model can accurately predict anomalies and surpass current State-Of-The-Art (SOTA) models regarding the quality of predictions and the time horizon considered to forecast them. © 2024 ieee.

关键词： Anomalies Forecasting Artificial Intelligence Data Center Graphs HPC Machine Learning

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A Deep-Learning-based Multi-modal ECG and PCG Processing Framework for Label Efficient Heart Sound Segmentation

A Deep-Learning-Based Multi-modal ECG and PCG Processing Fra...

引用

9th ieee/ACM international conference on Connected Health - Applications, systems and engineering Technologies (CHASE)

作者： Huang, Qijia Yang, Huanrui Zeng, Eric Chen, Yiran Duke Univ Dept Elect & Comp Engn Durham NC 27708 USA Univ Calif Berkeley Dept Elect Engn & Comp Sci Berkeley CA USA Duke Univ Dept Elect & Comp Engn Durham NC USA

ISBN: (纸本)9798350345025;9798350345018

The COVID-19 pandemic has intensified the need for home-based cardiac health monitoring systems. Despite advancements in electrocardiograph (ECG) and phonocardiogram (PCG) wearable sensors, accurate heart sound segmentation algorithms remain understudied. Existing deep learning models, such as convolutional neural networks (CNN) and recurrent neural networks (RNN), struggle to segment noisy signals using only PCG data. We propose a two-step heart sound segmentation algorithm that analyzes synchronized ECG and PCG signals. The first step involves heartbeat detection using a CNN-LSTM-based model on ECG data, and the second step focuses on beat-wise heart sound segmentation with a 1D U-Net that incorporates multi-modal inputs. Our method leverages temporal correlation between ECG and PCG signals to enhance segmentation performance. To tackle the label-hungry issue in AI-supported biomedical studies, we introduce a segment-wise contrastive learning technique for signal segmentation, overcoming the limitations of traditional contrastive learning methods designed for classification tasks. We evaluated our two-step algorithm using the PhysioNet 2016 dataset and a private dataset from Bayland Scientific, obtaining a 96.43 F1 score on the former. Notably, our segment-wise contrastive learning technique demonstrated effective performance with limited labeled data. When trained on just 1% of labeled PhysioNet data, the model pre-trained on the full unlabeled dataset only dropped 2.88 in the F1 score, outperforming the SimCLR method. Overall, our proposed algorithm and learning technique present promise for improving heart sound segmentation and reducing the need for labeled data.

关键词： heart sound (PCG) segmentation self-supervised learning multi-modal signal processing

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A Review of Advancements in AI-based Control Techniques for Microgrids 4

A Review of Advancements in AI-Based Control Techniques for ...

引用

4th international conference on Electrical, computer, Communications and Mechatronics engineering, ICECCME 2024

作者： Monchusi, Bessie Baakanyang Mokwana, Tlhokaboyo Innocentia University of South Africa Department of Electrical and Smart Systems Engineering Florida South Africa

ISBN: (纸本)9798350391183

Microgrids are among the most important sources of energy in developing countries, and their operation and management have become a key issue in the transition towards a sustainable and resilient energy future. The integration of artificial intelligence-(AI-) based control techniques offers unprecedented opportunities for enhancing energy resilience, sustainability, and efficiency. In this review paper, we provide insights into the recent developments and applications of AI-based control technologies for microgrid systems, including predictive energy management, distributed control architectures, adaptive learning, fault detection, and cybersecurity. The review of recent research highlights the effectiveness of AI in addressing key challenges in the operation and managing of microgriders, including distributed control strategies, and intelligent decision-making processes supported by robust algorithms, and communication frameworks. © 2024 ieee.

关键词： Decision making

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Educational Desktop for Remote Modeling of Secure Cyber-Physical systems Protocols 7

Educational Desktop for Remote Modeling of Secure Cyber-Phys...

引用

7th international conference on Information Technologies in engineering Education, Inforino 2024

作者： Frolov, Alexander Kochetova, Natalia Vinnikov, Alexander Sesiukalov, Alexander Institute of Information Technologies and Computer Science NRU MPEI Moscow Russia SoftEngineerig LLC Moscow Russia

ISBN: (纸本)9798350371369

The educational resource proposed in this work is intended for remote study of some combinatorial block designs and communication protocols of secure cyber-physical systems based on them, taking into account possible options for compromising keys and methods for capturing them. To construct such protocols and subsequently analyze them, five algorithms inherent in a balanced incomplete combinatorial block design (BIBD) of any type are used. These are algorithms for constructing a block, constructing a dual block, calculating the intersection of two blocks and the intersection of two dual blocks, as well as an algorithm for computing the list of blocks intersecting with each of two disjoint blocks (or two blocks truncated by removing a common block element). Five such algorithms for the unital BIBD are presented. We consider systems equipped with key ring systems or key space systems with a guaranteed security parameter. Schemes for the execution of some protocols with specified detail options are provided, as well as options for training assignments for such protocols. The educational resource allows you to remotely simulate processes in cyber-physical systems of both types, taking into account the specified restrictions concerning keys compromising and capturing. Such laboratory workshops play an important role and are an obligatory part of the educational process in the professional training of masters in applied mathematics and informatics. © 2024 ieee.

关键词： Cyber Physical System

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Motion Saliency based Human Action Recognition in RGBD Videos 20

Motion Saliency Based Human Action Recognition in RGBD Video...

引用

20th ieee international conference on Intelligent computer Communication and Processing conference, ICCP 2024

作者： Gutev, Alexander Debono, Carl James University of Malta Department of Communications and Computer Engineering Msida Malta

ISBN: (纸本)9798331539979

Human action recognition (HAR) systems need to process large volumes of data posing several challenges including, but not limited to, accurately identifying the actions and classifying them in near real time. Most of the state of the art solutions rely on machine learning techniques where a substantial amount of time is required both for the training and the inference phases. Existing solutions try to reduce the size of the data that is processed by the HAR classifier by constructing a spatio-temporal region of interest (ST-ROI) based on skeleton data, such as the model-based multimodal network (MMNet). However, the performance of these solutions depends on the accuracy of the skeleton data. This paper presents a novel solution to reduce the size of the data that is processed during classification by constructing an ST-ROI using regions identified as motion salient. The results show that the classification accuracy of the proposed solution is comparable to existing solutions such as MMNet but with a training time that is drastically lower. © 2024 ieee.

关键词： Spatio-temporal data

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A Survey on Application of Computational Technique for Two Area Interconnected Automatic Generation Control in Power System Using PSO-PID

A Survey on Application of Computational Technique for Two A...

引用

2025 ieee international Students' conference on Electrical, Electronics and computer Science, SCEECS 2025

作者： Yadav, Arpit Prasad, Lal Bahadur Kumar, Rajan Central Institute of Technology Kokrajhar Department of Electrical Engineering Assam Kokrajhar783370 India Madan Mohan Malaviya University of Technology Department of Electrical Engineering U.P. Gorakhpur273010 India

ISBN: (纸本)9798331529833

In this paper the various computational approaches for automatic generation control (AGC) of power systems is covers, with the goal of improving the automatic voltage regulator (AVR) and load frequency control (LFC) that are utilized with proportional integrative and derivatives (PID) controllers. PID tuning has long been a main area of current study, and PID controllers, sometimes known as proportional integrative and derivatives, are utilized in industry. The techniques being reviewed are conventional methods designed for PID based PSO optimization strategies that fit tuning requirements. Power engineering research and development now places a strong emphasis on optimization due to the growing requirement for energy. This paper deals on the electric power system applications of two area interconnected PSO based PID computational techniques to inspires power sector to uncover fresh information about the ideal rate of energy provision because of its creative application. © 2025 ieee.

关键词： Three term control systems

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An Anomaly Detection Method based on Meta-Path and Heterogeneous Graph Attention Network 5

An Anomaly Detection Method Based on Meta-Path and Heterogen...

引用

5th international conference on computer engineering and Application (ICCEA)

作者： Peng, Zheheng Shan, Chun Hu, Changzhen Beijing Inst Technol Sch Cyberspace Sci & Technol Beijing Peoples R China Beijing Inst Technol Sch Comp Sci & Technol Beijing Peoples R China

ISBN: (纸本)9798350386783;9798350386776

Advanced Persistent Threats (APT) in the current network environment are becoming increasingly complex and diverse. Most existing APT anomaly detection is based on attack knowledge bases and preset rules, which are difficult to design and cannot make good use of the rich semantic information in the original log data. This results in poor detection of unknown attacks. This paper proposes an anomaly detection method based on meta-path and heterogeneous provenance graph. We design a heterogeneous graph structure to represent provenance graph, and define the meta-paths of the PROCESS nodes. Then we use Heterogeneous Graph Attention Network (HAN) to learn the embedding representation of the nodes based on meta-paths. The resulting graph's node embedding is used as node features, and then we apply SVDD algorithm to identify anomalous nodes. A series of experiments were conducted on the Unicorn SC-2 dataset to validate the proposed method. The final results demonstrate that our method outperforms two current anomaly detection systems.

关键词： Anomaly Detection Provenance Graph meta-path Heterogeneous Graph Attention Network SVDD

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MapFormer: Attention-based multi-DNN Manager for Throughout & Power Co-Optimization on Embedded Devices 24

MapFormer: Attention-based multi-DNN Manager for Throughout ...

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43rd international conference on computer-Aided Design, ICCAD 2024

作者： Karatzas, Andreas Anagnostopoulos, Iraklis School of Electrical Computer and Biomedical Engineering Southern Illinois University Carbondale United States

ISBN: (纸本)9798400710773

In the context of modern services that use multiple Deep Neural Networks (DNNs), managing workloads on embedded devices presents unique challenges. These devices often incorporate diverse architectures, necessitating advanced management solutions to efficiently deploy multi-DNN workloads. Traditionally, the focus has been on improving throughput, while power optimization has received less attention. This paper presents MapFormer, a new manager that uses attention-based mechanisms to enhance both throughput and power efficiency. MapFormer intelligently assigns multi-DNN workloads to different computing components of embedded systems—CPU, GPU, and DLA—and adjusts operational frequencies to optimize power use. Experimental results show that MapFormer significantly improves average throughput under set power budgets by 90.8%, offering a promising approach for managing complex workloads on heterogeneous embedded systems. © 2024 Copyright is held by the owner/author(s).

关键词： Budget control

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