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检索条件"任意字段=5th IEEE International Conference on Distributed Computing in Sensor Systems"

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Proceedings of PAW-ATM 2022: Parallel Applications Workshop, Alternatives to MPI+X, Held in conjunction with SC 2022: the international conference for High Performance computing, Networking, Storage and Analysis

Proceedings of PAW-ATM 2022: Parallel Applications Workshop,...

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5th Annual ieee/ACM Parallel Applications Workshop, Alternatives to MPI+X, PAW-ATM 2022

ISBN: (纸本)9781665454100

the proceedings contain 5 papers. the topics discussed include: extending OpenMP and OpenSHMEM for efficient heterogeneous computing;task fusion in distributed runtimes;composition of algorithmic building blocks in template task graphs;asynchronous workload balancing through persistent work-stealing and offloading for a distributed actor model library;and design and performance evaluation of UCX for Tofu-D interconnect with OpenSHMEM-UCX on Fugaku.

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An Enhanced Smart Intelligent Detecting and Alerting System for Industrial Gas Leakage using IoT in sensor Network 5

An Enhanced Smart Intelligent Detecting and Alerting System ...

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5th international conference on Smart systems and Inventive Technology, ICSSIT 2023

作者： Senthil, G.A. Suganthi, P. Prabha, R. Madhumathi, M. Prabhu, S. Sridevi, S. Agni College of Technology Dept. of Information Technology Chennai India Sri Sai Ram Institute of Technology Dept. of Electronics and Communication Engineering Chennai India Karpaga Vinayaga College of Engineering and Technology Dept. of Artificial Intelligence and Data Science Chennai India Sri Ramachandra Engineering and Technology Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9781665474672

Leakage of any type of gas has emerged as a hazard in recent years, whether in a typical workplace, boiler, a commercial setting, a café, or a canteen, industries natural gas, coal, refinery products, crude oil, cement, electricity, steel, and fertilizers etc. In this article, I proposed a new invention of an IoT-based gas waste monitoring, leak detection, and alerting system. the research extends on the development of such an intelligent system that can intelligently preserve gas, ensure the safety precaution and pre vent accidents. the technique must be connected to the stove. the design incorporates ultrasonic sensors which identify bio gas leakage in circumstance with in square metre is usage area of industries or cooking place. the Proposed work smart intelligence system is cuts off the gas supply using an automated shutting off device. Whenever a gas leak is detected, consumers will be notified through SMS via GSM, location using GPRS, and will be able to resolve the situation as quickly as feasible. the flame sensor in the technology will supervise flame and fire. the buzzer starts to sound when a fire is detected. Aside from that, the system may save data on the cloud. With the help of this cloud storage solution, every user's daily gas use may be traced. Finally, this approach will aid in recognizing per user natural gas consumption. the system has been examined and is capable of monitoring gas loss, evacuation, and sending an SMS notification and location aid to the user. the precipitate demonstrated its usefulness in conserving a considerable amount of domestically squandered gas. © 2023 ieee.

关键词： Liquefied petroleum gas

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HeteroSAS: A Heterogeneous Resource Management Framework for "All-in-the-Air" Social Airborne Sensing in Disaster Response 17

HeteroSAS: A Heterogeneous Resource Management Framework for...

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17th Annual ieee international conference on distributed computing in sensor systems (ieee DCOSS)

作者： Rashid, Md Tahmid Zhang, Daniel (Yue) Wang, Dong Univ Notre Dame Dept Comp Sci & Engn Notre Dame IN 46556 USA

ISBN: (纸本)9781665439299

Social airborne sensing (SAS) is emerging as a new sensing paradigm that leverages the complementary aspects of social sensing and airborne sensing (i.e., UAVs) for reliable information collection. In this paper, we present HeteroSAS, a heterogeneous resource management framework for "all-in-the-air" SAS in disaster response applications. Current SAS approaches use UAVs to only capture data, but carry out computation on ground-based processing nodes that may be unavailable in disaster scenarios and thus consider a single model of UAV along with only one type of task (i.e., data capture). In this paper, we explore the opportunity to exploit the complementary strengths of different UAV models to accomplish all stages of sensing tasks (i.e., data capturing, maneuvering, and computation) exclusively "in-the-air". However, several challenges exist in developing such a resource management framework: i) handling the uncertain social signals in presence of the heterogeneity of UAVs and tasks;and ii) adapting to constantly changing cyber-physical-social environments. the HeteroSAS framework addresses these challenges by building a novel resource management framework that observes the environment and learns the optimal strategy for each UAV using techniques from multi-agent reinforcement learning, game theory, and ensemble learning. the evaluation with a real-world case study shows that HeteroSAS outperforms the state-of-the-art in terms of detection effectiveness, deadline hit rate, and robustness on heterogeneity.

关键词： Social networking (online) Computational modeling Reinforcement learning Robustness Data models sensor systems sensors

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A high sensitivity high-frequency current sensor design 4

A high sensitivity high-frequency current sensor design

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4th ieee international conference on Power, Intelligent computing and systems, ICPICS 2022

作者： Guowei, Li Nianbai, Liang Jiayi, Deng Guowei, Guo Shaohui, Liu Chun, Bei Foshan Power Supply Bureau of Guangdong Power Grid Co. LTD. Foshan China Guangdong University of Technology Electrical Engineering Department Guangzhou China Guangdong University of Technology Guangzhou China

ISBN: (数字)9781665467735

ISBN: (纸本)9781665467735

In this paper, based on the analysis of the working principle of high-frequency current transformer (HFCT), a high sensitivity small HFCT is designed for partial discharge (PD) detection of cable intermediate joints, and the factors affecting the sensitivity of the sensor are analyzed. the results of simulation analysis and experiments show that the sensor has good linearity in the passband, and the gain in the frequency range of 1MHz5MHz can reach more than 44dB (150mV/mA), with high sensitivity, and can couple out the small partial discharge current, which has strong practical application value. © 2022 ieee.

关键词： Partial discharges

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A CNN-inspired Reverse Search Engine for Content-based Image Retrieval 5

A CNN-inspired Reverse Search Engine for Content-based Image...

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5th international conference on Contemporary computing and Informatics, IC3I 2022

作者： Marathe, Shilpa Arosh, Sirshendu Mondal, Tamal DY Patil International University Pune Maharashtra Akurdi India West Bengal Asansol India Faculty of Computer Studies Symbiosis Center for Information Tech. Maharashtra Pune India

ISBN: (纸本)9798350398267

the Reverse Search Engine enables the search and retrieval schemas of images which are visually comparable to a specific query image by utilizing the features of the images that illustrates the visual content of provided images. this contribution creates a contextual schema for recovering photographs that are indexed in distributed databases from the visual information without the requirements of linguistic annotations. We implement the generic Convolutional neural network (CNN) with deep learning strategies for retrieving feature vectors from visual information. To build the global descriptors, the pre-trained CNN architectures i.e., ResNet50, InceptionV3, and InceptionResNetV2 were recommended. these techniques outperformed in the existing methods like Metaheuristic Algorithm, Gabor Wavelet, CNN-SVM which have been compared with the proposed technique i.e., Cartoon Texture Algorithm in CBIR. We have observed that they are similar to others based on machine learning approaches, with comprising of the conventional state-of-the-art in the Content-dependent Information Retrieval. © 2022 ieee.

关键词： Convolutional neural networks

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State-of-Charge Estimation of Lithium-Ion Batteries Using Machine Learning Based on Augmented Data 5

State-of-Charge Estimation of Lithium-Ion Batteries Using Ma...

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5th international conference on Smart Energy systems and Technologies (SEST)

作者： Pohlmann, Sebastian Karnehm, Dominic Mashayekh, Ali Kuder, Manuel Gieraths, Antje Weyh, thomas Univ Bundeswehr Inst Distributed Intelligent Syst Munich Germany Univ Bundeswehr Inst Elect Energy Syst Munich Germany

ISBN: (数字)9781665405577

ISBN: (纸本)9781665405577

For enhanced safety and efficiency, it is crucial to accurately monitor and determine the condition of a lithiumion battery (LIB). Because of dynamic operating conditions and several diverse aging mechanisms, it is necessary to further improve the algorithms of the battery management system. In this paper, machine learning (ML) is used to estimate the state of charge (SOC). As data-driven models highly depend on their data basis and battery tests are time and cost expensive, the data is enriched by simulation and augmented data. Based on this data, a linear regression, a support vector machine, and different types of artificial neural networks are trained. All models are tested on a real world data set. A lowest test error is obtained using the convolutional neural network with a test RMSE of 1.74 %. the results highlight the accuracy of data-driven models for the prediction of the battery state and the potential to improve the models by using data augmentation techniques.

关键词： Battery State of charge Battery Management System Machine learning Simulation

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A Single High Gain and Narrow-Beam Folded Dipole Antenna for Millimeter-Wave Applications 11

A Single High Gain and Narrow-Beam Folded Dipole Antenna for...

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11th ieee-APS Topical conference on Antennas and Propagation in Wireless Communications (ieee APWC) / 23rd international conference on Electromagnetics in Advanced Applications (ICEAA)

作者： Matsunaga, Mayumi Shizuoka Univ Hamamatsu Shizuoka Japan

ISBN: (数字)9781665481137

ISBN: (纸本)9781665481137

Millimeter wave applications are expanding, such as the 5th generation mobile communication system (5G), imaging technologies and RF sensors. Since wavelengths of millimeter waves are shorter than those of microwaves, it is necessary to increase radiation gains of antennas in order to secure the same communication distance as microwaves. For increasing antennas’ radiation gains, making beams narrow is effective. And the narrow beam width is necessary for high resolution imaging technologies.

关键词： Microwave antennas Wireless communication Radio frequency Dipole antennas Millimeter wave technology Microwave communication sensor systems and applications

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Research on fault diagnosis and fault tolerant control of PMSM current sensor 5

Research on fault diagnosis and fault tolerant control of PM...

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5th ieee international Electrical and Energy conference, CIEEC 2022

作者： Niu, Xiaobing Li, Shangbo Dalian Maritime University School of Marine Electrical Engineering Dalian China

ISBN: (数字)9781665411042

ISBN: (纸本)9781665411042

In conventional magnetic field directional control of permanent magnet synchronous motor (PMSM) systems, position and current sensors are required. However, the sensors are prone to various faults, which reduces the reliability. therefore fault diagnosis and fault-tolerant control for current sensors has attracted the attention of scholars as a new research area. To address this problem, a fault diagnosis scheme based on algebraic differential observation theory is proposed in this paper to monitor the operating status of current sensors. And combined with the current linear expended state observer (LESO) based on the idea of error reconstruction. the reconstruction space vector error projection is used as the correction term to complete the estimation of the fault phase current through the LESO for the current sensor fault diagnosis and fault-tolerant control. Finally, experiments are conducted on the dSPACE real-time simulation system as the core of the SPMSM platform to verify the effectiveness of fault diagnosis and fault-tolerant control. © 2022 ieee.

关键词： Failure analysis

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A Privacy Preserving System for AI assisted Video Analytics 5

A Privacy Preserving System for AI assisted Video Analytics

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5th ieee international conference on Fog and Edge computing (ICFEC)

作者： Lachner, Clemens Rausch, thomas Dustdar, Schahram TU Wien Distributed Syst Grp Vienna Austria

ISBN: (纸本)9781665402910

the emerging Edge computing paradigm facilitates the deployment of distributed AI-applications and hardware, capable of processing video data in real time. AI-assisted video analytics can provide valuable information and benefits for parties in various domains. Face recognition, object detection, or movement tracing art prominent examples enabled by this technology. However, the widespread deployment of such mechanism in public areas are a growing cause of privacy and security concerns. Data protection strategies need to he appropriately designed and correctly implemented in order to mitigate the associated risks. Most existing approaches focus on privacy and security related operations of the video stream itself or protecting its transmission. In this paper we propose a privacy preserving system for AI-assisted video analytics, that extracts relevant information from video data and governs the secure access to that information. the system ensures that applications leveraging extracted data have no access to the video stream. An attribute-based authorization scheme allows applications to only query a predefined subset of extracted data. We demonstrate the feasibility of our approach by evaluating an application motivated by the recent COVID-19 pandemic, deployed on typical edge computing infrastructure.

关键词： privacy artificial-intelligence edge-computing information-extraction video-processing attribute based authentication

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Differentially distributed Private Intelligence Security in Cybersecurity Infrastructures 5

Differentially Distributed Private Intelligence Security in ...

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5th international conference on Smart systems and Inventive Technology, ICSSIT 2023

作者： Jenifer, P. Geebin, A.R. Brundha, P. Manohar, E. Francis Xavier Engineering College Department of Cse Tirunelveli India

ISBN: (纸本)9781665474672

Novel ways of security against attacks to critical infrastructure are becoming more important as their number and sophistication increase. In order to provide the kind of security that is necessary, one must consider both preventive measures and an understanding of the current state of affairs inside the systems being protected. Researches and institutions have brought security solutions that are both intelligent and selfsufficient, such as the necessity for sophisticated proactive monitoring capability in Intrusion Detection systems (IDS). this study argues that critical infrastructures need automated IDS solutions and that adding Machine Learning (ML) approaches into IDS will be useful for developing dynamically adjustable algorithms that can promptly respond to changing circumstances and threats. To this end, this study assesses the several degrees of automation provided by the IDS and provide a plan for introducing preemptive automation into critical settings. Finally, this study evaluates how well the proposed method stacks up against other methods previously presented in the literature. Machine learning presents difficulties in the field of cybersecurity that must be effectively addressed using sound methodology and theory. Transfer learning, support vector machines, and Bayesian classification are just a few examples of the statistical and machine learning approaches that have been shown to be useful in reducing the effects of cyberattacks. It is crucial to establish smart security systems that can discover hidden patterns and insights from packet headers and then construct a data-driven machine-learning model to avoid these assaults. this study examines the use of machine learning methods to cybersecurity data with the goal of bolstering the safety of existing infrastructures. this article has addressed the current state of cybersecurity threats as well as the deployment of machine learning methods to combat the aforementioned challenges. © 2023 ieee.

关键词： Cybersecurity

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