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

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On the Characteristic Functions in Listing Stable Arguments 2nd

On the Characteristic Functions in Listing Stable Argument...

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2nd International conference on advanced Engineering, Technology and applications, ICAETA 2023

作者： Nofal, Samer Jabal, Amani Abu Alfarrarjeh, Abdullah Hababeh, Ismail Department of Computer Science German Jordanian University Amman Jordan

ISBN: (纸本)9783031509193

An abstract argumentation framework (af) is viewed as a directed graph such that graph vertices represent abstract arguments while graph edges denote attacks between these arguments. We say that a set, S, of arguments, are conflict-free if and only if for every (x, y) ∈ S× S, x does not attack y. A set, S, of arguments of a given af, is called a stable extension in af if S is conflict-free and such that every argument outside S is attacked by an argument inside S. To the best of our knowledge, a thorough mathematical analysis of the truth of what so-called characteristic functions (which are an essential component in generating all stable extensions of a given af) is not previously addressed in the literature. We fill this gap;we rigorously analyze the verity of characteristic functions employed in listing all stable extensions in a given af. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Characteristic Functions Computational Argumentation Multiagent systems Nonmonotonic Reasoning Stable Semantics

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Numerical Analysis on Slinky 3 dB Power Divider for High Speed RF sensor System

Numerical Analysis on Slinky 3 dB Power Divider for High Spe...

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2022 IEEE International conference on Electronics, Computing and Communication Technologies, CONECCT 2022

作者： Gurulakshmi, A.B. Rajesh, G. Sharma, Sanjeev New Horizon College of Engineering Department of Electronics & Communication Engineering Bangalore India

ISBN: (数字)9781665497817

ISBN: (纸本)9781665497817

The aim of the presented article is to design a high frequency 3db slinky power divider for RF sensor applications which operates at 6 GHz frequency. The material used for the design of proposed structure is FR4 material commonly used for PCB design. This material is identified to have medium refractive index in the order of 4.6 which ensures minimizes structure and compatible with the exciting systems. The purpose of the proposed power divider is to handle the power in RF sensor network to ensure optimal functioning at high frequency RF systems. The size and frequency handling ability of the power divider is confined to FR4 material due to the advantage of the compatibility of the available RF systems. The entire structure is mathematically analysed followed by numerical solving and fabrication. The design of novel 3db slinky power divider for RF sensor applications at high frequency which provides higher bandwidth that can be appreciated and recommended for real time wireless sensing networks. This work starts with the understanding of Electromagnetic fundamentals for RF system design and its parametric properties like Insertion Loss, Return Loss, current distribution, Near Field Crosstalk (NFC), Far Field Crosstalk (FEC), radiation pattern and Voltage Standing Wave Ratio (VSWR). Literature survey on various power divider for real time applications are studied. The need for the power divider circuits in RF sensors are identified and proposed a 3dB slinky power divider. Numerical solving of the proposed structure is done using the simulator and the validation of obtained results with the previous finding are done. Proper material selection is done based on the industry used products. advanced Design System (ADS) is used for simulation and parametric study of the power divider. The insertion loss with frequency plot with resonant frequency of 8.912 GHz is presented along with the return loss verses frequency plot. The proposed power divider is covering the frequency

关键词： Crosstalk

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advanced Piezoelectric PVDF Fiber Films with Te Nanowires for Sustainable Energy Harvesting

Advanced Piezoelectric PVDF Fiber Films with Te Nanowires fo...

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IEEE International conference on Power and Energy systems (ICPES)

作者： Yuanyuan Li Pengfei Fang Yumin Wang Siwei Pan School of Physics and Technology Wuhan University Wuhan China Electric Power Research Institute of Guangdong Power Grid Co. Ltd. Guangzhou China

ISBN: (数字)9798350391329

ISBN: (纸本)9798350391336

Piezoelectric polymers are valued for their ease of fabrication and integration into devices, making them highly promising for self-powered systems and sensor applications that rely on converting mechanical energy into electrical energy. However, their relatively low piezoelectric performance compared to piezoelectric ceramics has limited their practical use. In this study, we present Te-doped PVDF piezoelectric nanofiber mats fabricated through electrospinning, demonstrating enhanced piezoelectric properties. Flexible piezoelectric nanogenerators (PENGs) incorporating 8 wt% Te in PVDF show a notable increase in output voltage, from 1.2 V to approximately 4.2 V, under a 5 N pressure at 3 Hz. This improvement allows the fiber mats to effectively convert mechanical energy into electrical energy, generating an instantaneous output power density of around $45.7 \mu \mathrm{W} / \text{cm}^applications$ , making them suitable for self-powered sensing and energy harvesting applications.

关键词： Optical fiber sensors Power system measurements Nanogenerators Nanocomposites Voltage sensor systems Nanowires Mechanical energy sensors Wearable devices

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sensor based posture detection system

Sensor based posture detection system

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International conference on Integration of advanced Technologies for Industry 4.0 (ICIATI) - Materials Science

作者： Devi, K. Nirmala Anand, Jose Kothai, R. Krishna, J. M. Ajai Muthurampandian, R. KCG Coll Technol Chennai Tamil Nadu India

Posture identification system has been extensively useful in areas such as physical activities, ecological awareness events, man machine interfacing applications, control room panel monitoring systems and in autonomous old people caring systems. This paper describes a sensor based posture detection and guidance system while doing yoga exercise. In our day-to-day life, doing yoga exercise has become a well-known discipline, which helps people to be physically fit and have good mental health. Practicing yoga exercise is a recognized system for the human beings to improve their physical and mental activities. This practice helps in regulatory activities in an individual's mind, body and soul. As a result of this, flexibility, muscle power and body image will increase. The existing method has shown the recognition of posture analysis through virtual reality and exergame. We proposed a suit that is designed and implemented through a virtual master. The suit is integrated with various types of accelerometer sensors which are fixed on the flexible bands, that are light in weight and are used indirectly to measure the orientation of the limbs. Also the sensor integrated suit is very helpful to measure the direction of the tilt moment. Predefined calibrated values are loaded on to the microcontroller, which are compared with the measured values and when there is a match the information is sent to a mobile application. The mobile app identifies whether the user is doing the posture correctly or not based on the information given to practice the yoga exercise. (C) 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the International conference on Integration of advanced Technologies for Industry 4.0 : Materials Science

关键词： Mobile app Posture detection sensors Suit Yoga

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Niobium Pentaoxide Thin-Film Gas sensor for Portable Acetone Sensing 15

Niobium Pentaoxide Thin-Film Gas Sensor for Portable Acetone...

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15th International conference on advanced Technologies, systems and Services in Telecommunications, TELSIKS 2021

作者： Lombardo, Luca Grassini, Sabrina Parvis, Marco Donato, Nicola Gullino, Alessio Politecnico di Torino Department of Electronics End Telecommunications Torino Italy Politecnico di Torino Department of Applied Science and Technology Torino Italy University of Messina Department of Engineering Messina Italy

ISBN: (纸本)9781665444422

Acetone gas sensing finds application in several fields such as biomedical applications, food industry, chemical manufacturing and environmental monitoring. Often, such applications require good sensing performance and portability of the sensing devices. This paper proposes a conductometric gas sensor based on a thin-film of Nb2O5 able to detect acetone at the sub-ppm level with good selectivity and repeatability. The quite small dimensions and the low power consumption make the proposed sensor suitable for portable applications. © 2021 IEEE.

关键词： Medical applications

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Relevance Meets Diversity: A User-Centric Framework for Knowledge Exploration Through Recommendations 24

Relevance Meets Diversity: A User-Centric Framework for Know...

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30th ACM SIGKDD conference on Knowledge Discovery and Data Mining

作者： Coppolillo, Erica Manco, Giuseppe Gionis, Aristides Univ Calabria Dept Comp Sci Arcavacata Di Rende Italy ICAR CNR Arcavacata Di Rende Italy KTH Royal Inst Technol Div Theoret Comp Sci Stockholm Sweden

ISBN: (纸本)9798400704901

Providing recommendations that are both relevant and diverse is a key consideration of modern recommender systems. Optimizing both of these measures presents a fundamental trade-off, as higher diversity typically comes at the cost of relevance, resulting in lower user engagement. Existing recommendation algorithms try to resolve this trade-off by combining the two measures, relevance and diversity, into one aim and then seeking recommendations that optimize the combined objective, for a given number of items. Traditional approaches, however, do not consider the user interaction with the suggested items. In this paper, we put the user at the central stage, and build on the interplay between relevance, diversity, and user behavior. In contrast to applications where the goal is solely to maximize engagement, we focus on scenarios aiming at maximizing the total amount of knowledge encountered by the user. We use diversity as a surrogate for the amount of knowledge obtained by the user while interacting with the system, and we seek to maximize diversity. We propose a probabilistic user-behavior model in which users keep interacting with the recommender system as long as they receive relevant suggestions, but they may stop if the relevance of the recommended items drops. Thus, for a recommender system to achieve a high-diversity measure, it will need to produce recommendations that are both relevant and diverse. Finally, we propose a novel recommendation strategy that combines relevance and diversity by a copula function. We conduct an extensive evaluation of the proposed methodology over multiple datasets, and we show that our strategy outperforms several state-of-the-art competitors. Our implementation is publicly available(1).

关键词： User Modeling Diversity Recommender systems

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Transferring Gait Predictors Across EMG Acquisition systems with Domain Adaptation

Transferring Gait Predictors Across EMG Acquisition Systems ...

引用

International conference on Rehabilitation Robotics (ICORR)

作者： Guez, Annika Hodossy, Balint Farina, Dario Vaidyanathan, Ravi Imperial Coll London Dept Mech Engn London England Imperial Coll London Dept Biomed Engn London England

ISBN: (纸本)9798350342758

Lower limb assistive technology (e.g. exoskeletons) can benefit significantly from higher resolution information related to physiological state. High-density electromyography (HD-EMG) grids offer valuable spatial information on muscle activity;however their hardware is impractical, and bipolar electrodes remain the standard in practice. Exploiting information rich HD-EMG datasets to train machine learning models could help overcome the spatial limitations of bipolar electrodes. Unfortunately, differences in signal characteristics across acquisition systems prevent the direct transfer of models without a drop in performance. This study investigated Domain Adaptation (DA) to render EMG-based models invariant to different acquisition systems. This approach was evaluated using a Temporal Convolutional Network (TCN) that mapped EMG signals to the subject's knee angle, using HD-EMG as source data and Delsys bipolar EMG as target data. Furthermore, the feature extraction learnt by the TCN was also applied across muscle groups, evaluating the transferability of the sensor agnostic features. The DA implementation shows promise in both scenarios, with an average increase in accuracy (angular error normalised by the range of motion) of 7.36% for the Rectus Femoris, Biceps Femoris and Tibialis Anterior, as well as a cross-muscle performance increase of up to 10.80%. However, when the domain discrepancy is severe, the model is currently unable to generate a reliable walking trajectory due to inherent limitations related to the applied regression scheme and the chosen Mean Squared Error loss function. Therefore, future research should focus on exploring advanced loss functions and classification-based DA models that prioritise restoring key features of the gait.

关键词： Muscle

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Deep Learning-based Predictive Maintenance for Industrial IoT applications 7

Deep Learning-based Predictive Maintenance for Industrial Io...

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7th International conference on Inventive Computation Technologies, ICICT 2024

作者： Jeba Emilyn, J. Vinod Kumar, D. David Samuel Azariya, S. Prakash, M. Sam Thamburaj, A. Sona College of Technology Information Technology Tamil Nadu Salem India Biomedical Engineering Vinayaka Mission's Kirupananda Variyar Engineering College Tamil Nadu Salem India Computer Science Vinayaka Mission's Kirupananda Variyar Arts And Science College Tamil Nadu Salem India Vinayaka Mission's College of Physiotherapy Tamil Nadu Salem India

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359299

Integrating Deep Learning-based Predictive Maintenance (DL-PM) in Industrial Internet of Things (IoT) applications optimizes operational efficiency and reduces downtime. This reaserch work explores the synergy between deep learning and predictive maintenance within the Industrial IoT context. The study traces the evolution from traditional maintenance to modern deep learning methods facilitated by IoT's real-time data acquisition. Methodologically, it details data preprocessing, model selection, and design for DL-PM. Convolutional Neural Networks (CNNs) analyze sensor data, Recurrent Neural Networks (RNNs) predict time-series patterns and hybrid models incorporate transfer *** reaserch work demonstrates DL-PM's application across industries through diverse case studies, evaluating its performance and comparing it to conventional approaches. It highlights challenges like data quality, model interpretability, scalability, ethical concerns, and biases. Future directions encompass advanced deep learning techniques, edge-cloud integration, collaborative learning, and strategies to overcome challenges. DL-PM's potential to revolutionize industrial processes in the IoT era is *** reaserch work underscores the transformative impact of Deep Learning-based Predictive Maintenance in Industrial IoT. It provides insights into implementation, challenges, and prospects, guiding industries towards efficient, downtime-minimized operations through DL-PM integration. © 2024 IEEE.

关键词： Internet of things

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Statistical Conformance Checking of Aviation Cyber-Physical systems by Mining Physics Guided Models

Statistical Conformance Checking of Aviation Cyber-Physical ...

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IEEE Aerospace conference

作者： Banerjee, Ayan Maity, Aranyak Gupta, Sandeep K. S. Lamrani, Imane Arizona State Univ Sch Comp & Augmented Intelligence Tempe AZ 85281 USA Nikola Motor Co Phoenix AZ USA

ISBN: (纸本)9781665490320

Aviation systems such as aircrafts and Unmanned aerial vehicles (UAV) are cyber-physical systems (CPS) with several interacting automated control modules. The operational behavior of such CPS may change during deployment due to several reasons including: a) control system software bugs, b) environmental changes such as weather, and c) mechanical faults such as faulty sensor. Changes in operational characteristics can be exceptionally dangerous in civilian applications or when UAVs are used for military purposes or targeted precision tasks such as maneuvering in hostile environment. Moreover, safety violations are often not caused by a single fault but rather by a combination of faults that can potentially be avoided if the individual faults were pro-actively identified and mitigated. In this paper, we advocate for continuous verification of the operational characteristics to check its compliance with the specification. This compliance can be specified using Signal Temporal Logic (STL) on the output trajectories. However, unknown errors can cause operational changes that have little effect on the output characteristics but may manifest with fatal consequences when combined with other supervisory control systems. As such the problem of identifying such underlying changes is a difficult task specially when their impact on output characteristics is minimal. We propose a novel framework for analyzing the stochastic conformance of operational output characteristics of safety-critical CPS in terms of the coefficients of a physics guided surrogate model (PGSM) of the aerodynamics of the aviation CPS. We propose model guided hybrid recurrent neural networks (MGH-RNN) to mine a PGSM which is used to check the model conformance using STL on the model coefficients. The PGSM can also identify operational changes due to unknown errors. Additionally, we also derive a tractable input space partitioning mechanism based on PGSM characteristics to perform stochastic conformance checki

关键词： Cyber Physical System

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2022 International conference on advanced Robotics and Intelligent systems, ARIS 2022

2022 International Conference on Advanced Robotics and Intel...

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2022 International conference on advanced Robotics and Intelligent systems, ARIS 2022

ISBN: (纸本)9781665487184

The proceedings contain 15 papers. The topics discussed include: the gripping posture prediction of eye-in-hand robotic arm using Min-Pnet;open action recognition by a 3D convolutional neural network combining with an open fuzzy min-max neural network;evidential sensory fusion of 2D feature and 3d shape information for 3D occluded object recognition in robotics applications;spatially-excited attention learning for fine-grained visual categorization;spatially-excited attention learning for fine-grained visual categorization;calibration of a robot's tool center point using a laser displacement sensor;a computational approach for cam design parameters optimization of disk cam mechanisms with oscillating roller follower;a deep comparison network for visual prognosis of a linear slide;a swabbing robot for Covid-19 specimen collection;and genetic algorithm-determined artificial neural network architecture for predicting power usage effectiveness (PUE) in a data center.

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