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Anomaly Detection Method for Space Payload Parameters Under Telemetry Command Changes 3

Anomaly Detection Method for Space Payload Parameters Under ...

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3rd international conference on Advanced Manufacturing Technology and Electronic Information, AMTEI 2023

作者： Jin, Xin He, Yufeng University of Chinese Academy of Sciences Beijing100049 China Technology and Engineering Center for Space Utilization Chinese Academy of Sciences Beijing100094 China

ISBN: (纸本)9781510674806

To ensure the successful execution of on-orbit space missions, the operational data of space payloads must be continuously monitored. Due to the characteristics of interval-scaled variable parameters that change and fluctuate randomly with telemetry parameters, the traditional interval range anomaly detection method has low accuracy, and the deep learning-based approach is limited in the study of parameter changes with commands. In this paper, an anomaly detection method based on LSTM network model is proposed. First, encode command (CMD) into CMD Time Series and construct the corresponding dataset. Then, generate the parameter expectation values using LSTM and design the anomaly detection discriminative parameters and rules. By training the network model and setting the thresholds of the discriminative parameters according to the performance, we demonstrate the feasibility of the method by taking the actual parameters of an experimental payload as an example. © 2024 SPIE.

关键词： Anomaly detection

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ECG and sEMG Conditioning and Wireless Transmission with a Biosignal Acquisition Board 3rd

ECG and sEMG Conditioning and Wireless Transmission with a B...

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3rd international conference on Optimization, learning Algorithms and Applications (OL2A)

作者： Luiz, Luiz E. Coutinho, Fabio R. Teixeira, Joao P. Inst Politecn Braganca Res Ctr Digitalizat & Intelligent Robot CeDRI Campus Santa Apolonia P-5300253 Braganca Portugal Univ Tecnol Fed Parana Campus Toledo BR-85902490 Curitiba Parana Brazil Inst Politecn Braganca Lab Sustentabilidade & Tecnol Reg Montanha SusTEC Campus Santa Apolonia P-5300253 Braganca Portugal

ISBN: (纸本)9783031530357;9783031530364

The market for Wearable Health Monitoring Systems (WHMS) has grown together with the demand for devices that offer greater medical reliability and lower cost. This study introduces a wearable system comprising conditioning blocks for electrocardiogram and surface electromyogram signals, an analog-to-digital converter, and wireless data transmission capabilities. These features have been implemented reliably in accordance with the specific requirements of these signals, as well as complying with patient safety directives and ensuring the quality of the resulting signal, allowing it to be used as a data collector for subsequent software implementation. To evaluate its performance, this system is compared against commercially available wearable devices, and the expected outcomes are examined. The obtained results are then presented, showcasing the system's capabilities and leading to a positive conclusion. As future work, there is a focus on enhancing the user interface and implement digital processing in the result for use in pathology recognition software with greater accuracy.

关键词： Electrocardiogram ECG Electromyogram EMG Surface Electromyogram sEMG Signal Conditioning Wearable Health Monitoring Systems WHMS

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Deep learning Methods for Automatic Number Plate Recognition System: A Review 3

Deep Learning Methods for Automatic Number Plate Recognition...

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3rd international conference on intelligent engineering and Management, ICIEM 2022

作者： Poojary, Sushant Gaunkar, Manthan Naik Dessai, Babani Ayalli, Manju Pawar, Vijay Kumar Naik Aswale, Shailendra Shri Rayeshwar Institute of Engineering Information Technology Computer Engineering Shiroda Goa India Shri Rayeshwar Institute of Engineering Information Technology Shiroda Goa India

ISBN: (数字)9781665467568

ISBN: (纸本)9781665467568

Automatic number plate recognition(ANPR) is one of the smart transportation method that provides a safe medium of transportation and monitoring planning used by many two wheeler vehicles as well as four wheeler vehicles to boost traffic controlling, routing, parking system, toll collection, and governance and insuring highway law. There are various techniques and algorithms has been developed so that we can identify and recognize Number plates. This paper shows a various deep learning approach for recognizing and detecting number plate. This system is divided into three parts, i.e. number plate detection, segmentation, and character recognition. In our proposed system for number plate detection Yolo method is used, after that some filters are applied and then characters are segmented. Finally the Convolutional Neural Network is used to recognize all the segmented characters. © 2022 IEEE.

关键词： Convolutional neural networks

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An Experimental Analysis to Measure the Impact of data Size on Accuracy of Deep learning Model to Detect and Classify Monkey-Pox Using Skin Lesion Images 3

An Experimental Analysis to Measure the Impact of Data Size ...

引用

3rd international conference on Technological Advancements in Computational Sciences, ICTACS 2023

作者： Kaur, Gurpreet Handa, Disha Jalodia, Vikas Shukla, Anand Kr. University Institute of Computing Chandigarh University India School of Engineering and Technology Noida International University India

ISBN: (纸本)9798350342338

As a zoonotic disease, Monkey pox is caused by an orthodox virus. This is communicable disease and highly contagious. Detection of the virus at the early stage may stop its spread in the large community. Rapid and accurate diagnosis of monkey-pox is important for effective management and control of outbreaks. Deep learning techniques, a sub-branch of AI, are especially fruitful in visual detection research and many researchers have done fruitful effort in this area. In this study, with the help of experiments, we are doing comparative analysis of detection techniques based on size of dataset. The objective behind is to validate the relationship between accuracy and size of the data. In this study, the analysis is based on the relationship of the size of dataset and accuracy of the model. In order to make observations we have used two different sized datasets and observed that the increase of data of dataset size increases the accuracy of the model. © 2023 IEEE.

关键词： classification Deeplearning Detection Machine learning Monkeypox

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A Method of Extracting Outer Eye Corners of Terra Cotta Warriors Based on Point Cloud data 3

A Method of Extracting Outer Eye Corners of Terra Cotta Warr...

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3rd international conference on Image Processing and intelligent Control, IPIC 2023

作者： Cheng, Yue Liu, Xianglei Key Laboratory for Urban Geomatics of National Administration of Surveying Mapping and Geoinformation Engineering Research Center of Representative Building and Architectural Heritage Database Ministry of Education Beijing University of Civil Engineering and Architecture Beijing100044 China

ISBN: (纸本)9781510668140

The Terracotta Warriors and Horses of the First Qin Emperor are a highly prized relic in China and the world. Some people believe that the Terracotta Warriors are a realistic representation of the Qin dynasty, while others feel that the Terracotta Warriors are the result of artistic re-creation. Therefore, in order to verify the "realism" of the terracotta warriors and horses, the degree of resemblance between the terracotta warriors and real people was quantified. The representative points of the outer corners of the eyes on the head and face of the terracotta warriors are selected. The method of extracting feature points based on the cross-sectional line of approximation is proposed using point cloud data. The method is based on the approximation method of cross-sectional line extraction, which is more accurate and easier to calculate by converting 3D into 2D. The final experimental results show that the curvature of the outer corner points of the terracotta warriors and the curvature pattern of the real eyes basically match. The method verifies that the facial features of terracotta warriors are highly correlated with those of real people. At the same time, the "realistic" nature of the terracotta warriors is demonstrated. © 2023 SPIE.

关键词： data mining

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In-depth Analysis of Information Overflows and its Role in Shaping Future Knowledge Structures 3

In-depth Analysis of Information Overflows and its Role in S...

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3rd IEEE international conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Paranthaman, V. Kamal, M.D. Raj Balaji, V. Sunil, G. Adhi College of Engineering and Technology Dept. of Mechanical Engineering TN Chennai India Velammal Institute of Technology Department of Mechanical Engineering TN Chennai India Loyola Institute of Technology Department of Mechanical Engineering TN Chennai India School of Computer Science and Artificial Intelligence Sr University Telangana Warangal India

ISBN: (纸本)9798350343274

This study provides a thorough evaluation of data deluges and how they will play a significant role in the development of future knowledge architectures. The suggested methodology is adaptable and interdisciplinary since it combines cutting-edge data mining, NLP, and network analysis methods. data mining, NLP, network analysis, content analysis, big data analytics, cognitive mapping, SNA, MNA, QR, and IV are all compared to the proposed method, as are machine learning algorithms, qualitative research methods, and data visualization. Accuracy, relevance, depth of analysis, innovation, practical consequences, and clarity are only a few of the areas where the suggested technique shines. Its greater analytical skills are shown by its persistent outperformance of conventional approaches. Additionally, the suggested approach better accords with ethical norms and responsible research procedures, thus they are all considered as well. In summary, this study demonstrates the value of looking at information floods from several angles of study. The suggested technique not only provides cutting-edge analytic instruments for negotiating the intricacies of the digital information ecosystem, but also gives a forward-looking viewpoint by anticipating new patterns and developments. It demonstrates how crucial it is to use cutting-edge methods and evolve with the information age. © 2023 IEEE.

关键词： Natural language processing systems

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A Deep Reinforcement learning-Based Approach for Autonomous Quadrotor Tracking Control 1

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3rd international conference on Autonomous Unmanned Systems, ICAUS 2023

作者： Deng, Lan Wang, Jiaqing Jiang, Shuang Guo, Shuang Peng, Ao College of Mechanical and Electrical Engineering Hohai University Changzhou213022 China Research Institute of Management Science Business School Hohai University Nanjing210098 China College of Internet of Things Engineering Hohai University Changzhou213022 China

ISBN: (数字)9789819710874

ISBN: (纸本)9789819710867

Autonomously tracking a dynamic unmanned ground vehicle (UGV) with an unmanned aerial vehicle (UAV) is challenging due to the difficulty for the UAV to track the UGV’s real-time state and adjust its policy accordingly. In this paper, we propose a deep reinforcement learning approach for a quadrotor UAV to track a moving UGV without knowing its motion dynamics. The proposed learning framework consists of two main systems: a Tracking Vision System (TVS) utilizing deep learning and a Tracking Control System (TCS) utilizing deep reinforcement learning. The TVS recognizes the UGV by analyzing images captured by the UAV, and then it provides the TCS with input data that is used to fulfill Markov Decision Process (MDP) in the TCS. The TCS generates optimal landing policies learned through simulated training. Moreover, we propose an Expert Knowledge Transfer (EKT) algorithm that allows human intervention in the UAV’s action space, which can speed up the training process. We demonstrate the effectiveness of the proposed method in simulation experiments. © Beijing HIWING Scientific and Technological Information Institute 2024.

关键词： Reinforcement learning

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Reproduction of Experimental Tests for Tree-Line Conflicts in 10kV Distribution Lines 3

Reproduction of Experimental Tests for Tree-Line Conflicts i...

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3rd international conference on intelligent Power and Systems, ICIPS 2023

作者： Guo, Bosen Li, Tianyou Huang, Chaoyi Wang, Lianhui Chen, Shichuan School of Electrical Engineering and Automation Xiamen University of Technology Xiamen China State Grid Fujian Electric Power Company Fuzhou China

ISBN: (纸本)9798350328691

With the continuous construction of the power grid, a large number of power lines pass through forested areas. Electric sparks caused by power line faults may lead to forest fires, while forest fires can also cause power line failures. However, recording long-term waveforms of tree line faults and investigating the causes of forest fires are challenging in practice. To address this issue, we have chosen medium-voltage lines as the research focus and developed a simulation platform to simulate the interaction between power lines and trees. We have also established corresponding Simulink simulation circuits to compare measured data from the simulation platform with idealized conditions, allowing us to study the electrical parameter characteristics when power lines come into contact with trees. Additionally, we conducted experiments on distribution line faults under flame exposure and compared the observable traces on conductors between scenarios of contact with trees and exposure to flames. The findings of this research can serve as a reference for analyzing the combustion traces of steel-cored aluminum stranded wires in fire environments and identifying the causes of fires. ©2023 IEEE.

关键词： Simulation platform

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WiFi human gesture recognition based on SSC-SGRU 3

WiFi human gesture recognition based on SSC-SGRU

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3rd international conference on Electronic Information engineering, Big data, and Computer Technology, EIBDCT 2024

作者： He, Yulang Zhao, Zhibiao Li, Shanshan Li, Zhen Tianjin Key Laboratory of Information Sensing and Intelligent Control Tianjin University of Technology and Education No. 1310 Dagu South Road Tianjin300222 China School of Automation and Electrical Engineering Tianjin University of Technology and Education No. 1310 Dagu South Road Tianjin Tianjin300222 China

ISBN: (纸本)9781510680449

The Wi-Fi-based wireless sensing technology has emerged as a research hotspot in the field of perception in recent years, enabling intelligent sensing of human activities and the surrounding environment. However, existing wireless sensing models exhibit a high number of parameters, making real-time perception challenging, especially in scenarios with limited computational resources such as mobile devices. To address this issue, this paper proposes a classification recognition algorithm based on a hybrid approach that combines Spatially Separable Convolution (SSC) and Stacked Gate Recurrent Unit (SGRU). In the shallow layers of the hybrid model, the algorithm utilizes a feature extraction module composed of spatially separable convolution to capture spatial features of human gestures while maintaining the temporal consistency of features. In the deeper layers, SGRU network is employed to learn the spatiotemporal features of gestures. The SGRU consists of two layers, where the output of the first layer serves as the input to the second layer. Through validation on the open-source Widar dataset of human gestures, this paper demonstrates that the proposed SSC-SGRU, when compared to the comprehensive ResNet18 with a reduction of approximately 11.0M parameters, achieves an accuracy improvement of approximately 7.2%. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Channel state information

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Mild Cognitive Impairment Detection from EEG Signals Using Combination of EMD Decomposition and Machine learning 3

Mild Cognitive Impairment Detection from EEG Signals Using C...

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3rd international conference on Emerging Smart Technologies and Applications, eSmarTA 2023

作者： Aljalal, Majid Aldosari, Saeed A. Molinas, Marta Alsharabi, Khalil Alturki, Fahd A. Electrical Engineering King Saud University Riyadh Saudi Arabia Engineering Cybernetics Norwegian University of Science and Technology Trondheim Norway

ISBN: (纸本)9798350305333

Mild cognitive impairment (MCI) is the earliest stage of dementia, and its detection is crucial for disease management. Electroencephalography (EEG) has gained popularity as a tool for identifying brain disorders. This article presents methods for diagnosing MCI from EEG signals using empirical mode decomposition (EMD). The proposed methods are tested using EEG data from 32 healthy control (HC) cases and 29 MCI cases. First, major artifacts are removed through preprocessing. EMD is then used to decompose signals into intrinsic mode functions (IMFs), from which features are extracted using one of the following measures: energy, log energy entropy, sure entropy, threshold entropy, Shannon entropy, or band power. Multiple machine learning approaches are then used to classify whether the resulting features are for MCI or HC. Results reveal that the proposed methods, especially the one that combines EMD and entropy, produce competitive results. For instance, the achieved results for a KNN classifier using sure entropy in terms of classification accuracy, specificity, sensitivity, and F-score are 97.60%, 98.59%, 96.49%, and 97.44%, respectively. The findings demonstrate accurate detection of MCI using the proposed methods, indicating potential for clinical diagnosis applications. © 2023 IEEE.

关键词： Empirical mode decomposition

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