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检索条件"任意字段=Conference on Advanced Sensor Systems and Applications VII"

共 4668 条记录，以下是641-650 订阅

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Analysis of dispersion of wave propagation in urban street canyons via the modal expansion approach 10

Analysis of dispersion of wave propagation in urban street c...

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10th IEEE-APS Topical conference on Antennas and Propagation in Wireless Communications (APWC) / 22nd International conference on Electromagnetics in advanced applications (ICEAA) / USNC-URSI Radio Science Meeting (USNC-URSI RSM)

作者： Di Simone, Alessio Franceschetti, Giorgio Iodice, Antonio Univ Napoli Federico II Naples Italy

For the design of next-generation wireless communication systems (5G microcells, wireless sensor networks, device-to-device systems) in built-up areas, a key role is played by the peculiar propagation conditions withi... 详细信息

ISBN: (纸本)9781665413886

关键词： Wireless communication Wireless sensor networks 5G mobile communication Propagation Microcell networks Electromagnetics Next generation networking

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Leveraging Memory and Attention in a Kinematically Aware Robot: An Ideomotor-Inspired Approach to Implicit Command Understanding from IMU sensor Data*

Leveraging Memory and Attention in a Kinematically Aware Rob...

引用

IEEE/ASME (AIM) International conference on advanced Intelligent Mechatronics

作者： Lena Guinot Ryutaro Matsumoto Shota Takahashi Hiroyasu Iwata Graduate School of Creative Science and Engineering Waseda University Tokyo Japan Faculty of Science and Engineering Waseda University Tokyo Japan

ISBN: (数字)9798350355369

ISBN: (纸本)9798350355376

As human-robot interaction (HRI) advances, the nuanced interpretation of implicit commands embedded in human gestures becomes paramount for fostering seamless collaboration. In this context, we present a novel machine learning algorithm designed to endow robots with the ability to decipher implicit commands from Inertial Measurement Unit (IMU) sensor data worn at specific locations on the human body. Our approach integrates memory and attention mechanisms inspired by ideomotor cues, allowing the robot to comprehend both temporal and spatial relationships within the sensor data. The attention mechanism operates bidirectionally, enhancing the system’s awareness of the temporal sequence of human movements and the spatial interdependencies between sensor data across different body locations. This unique spatial attention enables the robot to understand the kinematic chain between joints during human motion, accommodating variations in sensor data arising from factors such as height differences and motion range capacity. Drawing on prior research in attention mechanisms, ideomotor cues, and memory augmentation, our algorithm represents a significant advancement in addressing the challenges of implicit command understanding in HRI. The proposed system’s adaptability and nuanced comprehension of human gestures make it wellsuited for diverse anatomies and movement patterns. Through comprehensive experiments, we demonstrate the effectiveness of our algorithm, paving the way for more intuitive and adaptable robotic systems in real-world applications.

关键词： Attention mechanisms Mechatronics Measurement units Machine learning algorithms Human-robot interaction Collaboration Kinematics

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Decoupled Unknown-Input Observer for Takagi-Sugeno systems: Application to Three Phase Interleaved Boost Converter

Decoupled Unknown-Input Observer for Takagi-Sugeno Systems: ...

引用

Electrical Engineering and advanced Technology (ICEEAT), International conference on

作者： Wail Hamdi Mohamed Yacine Hammoudi Salah Okba Ramzi Saadi MSE Laboratory Mohamed Khider University of Biskra Biskra Algeria

This paper presents the application of a Takagi-Sugeno based Decoupled Unknown-Input Observer for Three-Phase Interleaved Boost Converters for fuel cell applications. Harnessing the potential of a single sensor, the proposed methodology efficiently estimates all the remaining system states and the unknown input. This unique implementation of the Decoupled Unknown-Input Observer in the Interleaved Boost Converters ensures precise state estimation while effectively addressing system nonlinearities. Moreover, an observer based controller is implemented to regulate the DC-bus voltage converter, and its performance is confirmed through simulation test. The proposed work promises to enhance the reliability and cost-effectiveness of power conversion systems.

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Preserving Data Integrity and Security in IoT-Enabled Wireless sensor Networks: Employing an Intrusion Detection System Strategy

Preserving Data Integrity and Security in IoT-Enabled Wirele...

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Smart Power Control and Renewable Energy (ICSPCRE), IEEE International conference on

作者： M. Vaithiyanathan Naduvathezhath Nessariose Jose C.S. Sasireka Pooja Mishra T. Prabahar Godwin James Nookala Venu Department: Information Technology Sri Sai Ram Engineering College Regent Strategic Solutions Rochester USA Department Of Computer Science and Engineering SRM Institute of Science and Technology Management and Research DY Patil Institute of Engineering Pune Department Of Computer Science and Engineering Sri Sairam Institute of Technolgoy Centre for IoT Madhav Institute of Technology & Science (MITS) A Govt. Aided UGC Autonomous Institute Gwalior Madhya Pradesh India

ISBN: (数字)9798350377002

ISBN: (纸本)9798350377019

The proliferation of IoT-enabled wireless sensor networks (WSNs) has ushered in a multitude of applications but has also heightened concerns regarding data integrity and security. This underscores the critical necessity for robust Intrusion Detection systems (IDS) tailored explicitly for such environments. This study presents an innovative IDS framework designed to safeguard the integrity and security of data within -enabled. Our IDS framework introduces advanced machine learning techniques and anomaly detection algorithms to enable real-time monitoring and analysis, effectively identifying and mitigating potential threats. Through extensive experimentation and validation, we demonstrate the reliability and efficacy of our IDS in detecting various intrusion attempts while minimizing false alarms. Moreover, our framework incorporates adaptive mechanisms to address evolving threats and network dynamics, ensuring continuous protection against malicious activities. By enhancing the resilience of IoT-enabled WSNs against intrusions, our research contributes to bolstering trust and reliability in critical applications.

关键词： Wireless sensor networks Machine learning algorithms Data integrity Heuristic algorithms Intrusion detection Machine learning Real-time systems

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Toward Efficient Automated Feature Engineering 39

Toward Efficient Automated Feature Engineering

引用

39th IEEE International conference on Data Engineering, ICDE 2023

作者： Wang, Kafeng Wang, Pengyang Xu, Chengzhong Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences University of Macau China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen China State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science University of Macau China

ISBN: (纸本)9798350322279

Automated Feature Engineering (AFE) refers to automatically generate and select optimal feature sets for downstream tasks, which has achieved great success in real-world applications. Current AFE methods mainly focus on improving the effectiveness of the produced features, but ignoring the low-efficiency issue for large-scale deployment. Therefore, in this work, we propose a generic framework to improve the efficiency of AFE. Specifically, we construct the AFE pipeline based on reinforcement learning setting, where each feature is assigned an agent to perform feature transformation and selection, and the evaluation score of the produced features in downstream tasks serve as the reward to update the policy. We improve the efficiency of AFE in two perspectives. On the one hand, we develop a Feature Pre-Evaluation (FPE) Model to reduce the sample size and feature size that are two main factors on undermining the efficiency of feature evaluation. On the other hand, we devise a two-stage policy training strategy by running FPE on the pre-evaluation task as the initialization of the policy to avoid training policy from scratch. We conduct comprehensive experiments on 36 datasets in terms of both classification and regression tasks. The results show 2.9% higher performance in average and 2x higher computational efficiency comparing to state-of-the-art AFE methods. © 2023 IEEE.

关键词： Reinforcement learning

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A CNN-Based Approach for Early Detection of Breast Cancer Using Infrared Imaging

A CNN-Based Approach for Early Detection of Breast Cancer Us...

引用

Intelligent systems and advanced applications (ICISAA), International conference on

作者： Naman Nayak Deepak Kumar Abhiraj Malhotra Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura Punjab India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India

ISBN: (数字)9798331539948

ISBN: (纸本)9798331539955

Breast cancer remains a disease that affects women’s health worldwide and is a major cause of both new cancer cases and cancer-related deaths. Any advancement in early detection is crucial for both improved survival and the effectiveness of the appropriate treatments. This research aims to investigate the use of CNNs in infrared image-based breast cancer diagnosis, as well as the application of deep learning to improve cancer diagnosis outcomes. The study uses a number of related databases, mammography pictures, and histopathological data, including information from The Cancer Genome Atlas (TCGA) and the Digital Database for Screening Mammography (DDSM). Among the techniques frequently employed in the field of CNNs to enhance the model’s performance and work with the input information are normalisation and augmentation. Convolutional, pooling, and fully connected layers are the main layers used by the CNN structure to extract characteristics from and classify breast cancer images. The model’s specificity, which is assessed, is ${9 1 \%}$, and its recall and precision reveal a decent foundation that indicates the model can distinguish between benign and malignant instances. In addition, real-time thermal imaging sensor data and patient data acquired via RFID tags are used in the research to supplement the dataset and improve clinical applicability.

关键词： Databases Neural networks Genomics Infrared imaging Thermal sensors Delta-sigma modulation Breast cancer Real-time systems RFID tags Intelligent systems

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Enhancing Energy Efficiency and Security in Wireless sensor Networks: A Survey and Proposed Methodology Integrating Prediction Algorithms and Deep Neural Networks

Enhancing Energy Efficiency and Security in Wireless Sensor ...

引用

Cybernetics, Cognition and Machine Learning applications (ICCCMLA), IEEE International conference on

作者： Shaik Shafiuddin Konda Hari Krishna Department of Computer Science and Engineering School of Computing Mohan Babu University Tirupati Andhra Pradesh India

ISBN: (数字)9798331505790

ISBN: (纸本)9798331505806

This research study provides a complete assessment of energy- and trust-aware techniques in IoT-WSNs, emphasizing the significant problems and limits of current methodologies. Traditional methodologies frequently face challenges such as excessive processing overhead, difficulty reaching real-time optimal solutions, and increased delay, especially in large-scale or resource-constrained contexts. To solve these issues, the paper suggests a new approach that combines powerful prediction algorithms, deep neural networks, and strong security frameworks. The process entails initializing exact inputs and simulation settings, modeling advanced prediction algorithms, developing deep neural networks, and constructing resilient sensor nodes, all while implementing extensive security measures. The suggested approach seeks to considerably improve energy efficiency, network lifetime, and security by focusing on mobile sink movement prediction and network operation optimization. This study not only covers existing methodologies, but also shows the promise of the proposed methodology through extensive analysis and preliminary results, demonstrating its ability to overcome the limits of present WSN deployments.

关键词： Surveys Wireless sensor networks Artificial neural networks Predictive models Prediction algorithms Real-time systems Energy efficiency Stability analysis Security Optimization

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Open Loop Resonator Loaded Self Complementary Antenna With T-Shaped Dipoles for sensor applications

Open Loop Resonator Loaded Self Complementary Antenna With T...

引用

International conference on advanced Computing and Communication systems (ICACCS)

作者： Jolly Rajendran K H Minu Keerthana Mohan U Department of Electronics and Communication Engineering ACE College of Engineering Thiruvananthapuram India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

This paper proposes a novel antenna, which can be used for detecting electromagnetic field signatures associated with lightning strikes. The proposed printed antenna demonstrates sensitivity to electromagnetic emissions within the frequency range between 330 MHz to 960 MHz. To enhance the antenna's performance, OLRs (Open Loop Resonators) are integrated into a conventional self-complementary antenna (SCA) having T- shaped dipoles. The resulting antenna, the OLR- loaded self- complementary antenna with T-shaped dipoles (OSCTDA) is compact and exhibits a VSWR bandwidth spanning from 330 MHz to 960 MHz, maintaining a Voltage Standing Wave Ratio (VSWR) below 2 throughout the entire frequency spectrum. The antenna's performance is examined through simulations and experiments. The proposed antenna can be used to analyze electromagnetic emissions generated by lightning-induced surge currents.

关键词： Wireless communication Sensitivity Dipole antennas Resonant frequency Bandwidth Voltage Resonators Electromagnetics Surges Loaded antennas

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Digital Twins of Unmanned Aerial Vehicle Telemetry sensors

Digital Twins of Unmanned Aerial Vehicle Telemetry Sensors

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International conference on Industrial Engineering, applications and Manufacturing (ICIEAM)

作者： O. G. Shishkin V. M. Khachumov A.K.Aylamazyan Institute of Software Systems of the Russian Academy of Sciences s. Veskovo Moscow Russia Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences s. Veskovo Moscow Russia MIREA - Russian Technological University The Ministry of Education and Science Russia RUDN University s. Veskovo Moscow Russia

ISBN: (数字)9798331512194

ISBN: (纸本)9798331512200

In the era of the fourth industrial revolution, companies strive to optimize their operations by integrating advanced information technologies, which are rapidly growing in popularity. The use of these technologies enables organizations to improve the efficiency of production and administrative operations. A digital twin, which is a virtual copy of a physical object, helps high-tech enterprises move to a qualitatively new level of technological development and strengthen their competitive position in the market. Data analysis shows that due to the dynamic development of information technology and the digitalization of the economy, the demand for digital twins will steadily increase in the future. The paper proposes a method for constructing digital twins of telemetry sensors of unmanned aerial vehicles for aerospace purposes, and describes the prospects for their application.

关键词： Sensitivity Process control Production Autonomous aerial vehicles sensor systems Digital twins sensors Telemetry Information technology Vehicle dynamics

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Early Quality Prediction using Deep Learning on Time Series sensor Data 55

Early Quality Prediction using Deep Learning on Time Series ...

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55th CIRP conference on Manufacturing systems, CIRP CMS 2022

作者： Saadallah, Amal Abdulaaty, Omar Büscher, Jan Panusch, Thorben Morik, Katharina Deuse, Jochen Artificial Intelligence Group TU Dortmund University Otto-Hahn-Str. 12 Dortmund44227 Germany Institute of Production Systems TU Dortmund University Leonhard-Euler-Str. 5 Dortmund44227 Germany Centre for Advanced Manufacturing University of Technology Sydney Sydney Australia

In manufacturing systems, early quality prediction enables the execution of corrective measures as early as possible in the production chain, avoiding thus costly rework and waste of resources. The increasing development of Smart Factory sensors and Industrial Internet of Things has offered wide opportunities for applying data-driven approaches for early quality prediction in real-time using Machine Learning (ML). With the multiplication of applications, further requirements on the quality of predictive ML models covering multiple aspects such as accuracy, robustness and explainability have to be fulfilled to build trustworthy ML-based solutions. In this context, we investigate the task of early quality classification using a Convolutional Neural Network (CNN) on time series sensor data of an automotive real-world case study. To do so, a gradient-based heat-mapping explanation method for CNNs is computed to determine the most discriminative time series patterns and localize them in time. These patterns are subsequently used to achieve quality prediction in real-time as early as possible. © 2022 The Authors. Published by Elsevier B.V.

关键词： Automotive Manufacturing Deep Learning Early Quality Prediction Heat-maps sensor Data Time Series

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