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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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Enhancement of sensor-based User Identification using Data Augmentation Techniques 7

Enhancement of Sensor-based User Identification using Data A...

引用

7th international conference on Digital Arts, Media and Technology, DAMT 2022 and 5th ECTI Northern Section conference on Electrical, Electronics, Computer and Telecommunications Engineering, NCON 2022

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Faculty of Applied Science Department of Mathematics Bangkok Thailand

ISBN: (纸本)9781665495103

Mobile devices such as smartphones are increasingly being used to record personal, delicate, and security information such as images, emails, and payment information due to the growth of wearable computing. It is becoming more vital to employ smartphone sensor-based identification to safeguard this kind of information from unwanted parties. In this study, we propose a sensor-based user identification approach based on individual walking patterns and use the sensors that are pervasively embedded into smartphones to accomplish this. Individuals were identified using a convolutional neural network (CNN). Four data augmentation methods were utilized to produce synthetically more data. these approaches included jittering, scaling, and time-warping. We evaluate the proposed identification model's accuracy, precision, recall, F1-score, FAR, and FRR utilizing a publicly accessible dataset named the UIWADS dataset. As shown by the experiment findings, the CNN with the time-warping approach operates with very high accuracy in user identification, with the lowest false positive rate of 8.80 % and the most incredible accuracy of 92.7%. © 2022 ieee.

关键词： Smartphones

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Deep Reinforcement Learning for Distribution Power System Cyber-Resilience via distributed Energy Resource Control 14

Deep Reinforcement Learning for Distribution Power System Cy...

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14th ieee international conference on Communications, Control, and computing Technologies for Smart Grids, SmartGridComm 2023

作者： Corrado, Nicholas E. Livesay, Michael Bailey, Tyson Johnson, Jay Levin, Drew University of Wisconsin Department of Computer Sciences Madison United States Sandia National Laboratories United States

ISBN: (纸本)9781665455541

Interoperable, internet-connected distributed energy resource (DER) devices in power systems create a new attack vector for malicious cyber-actors. Modern control systems primarily focus on transmission and sub-transmission operations and rarely incorporate DER or other distribution-connected equipment. While recent advances have expanded grid operator visibility and control functionality to the distribution level, these control systems struggle to scale with thousands of networked devices. thus, to defend against potential attacks on DER devices, we must develop new real-time control algorithms to ensure grid stability. In this work, we present a new approach to power distribution control based on deep reinforcement learning (RL) algorithms which can learn optimal control policies through experience. We evaluate four RL algorithms in novel voltage stabilization tasks based on the ieee 13-bus and EPRI Ckt5 power distribution models. We demonstrate that RL can successfully perform voltage regulation and outperform a greedy algorithm. Our results coupled with the established capability of RL to scale in high-dimensional settings opens a path towards real-time cyber defense of power systems with RL agents. © 2023 ieee.

关键词： Reinforcement learning

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AI Processor based Data Correction for Enhancing Accuracy of Ultrasonic sensor

AI Processor based Data Correction for Enhancing Accuracy of...

引用

ieee international conference on Artificial Intelligence Circuits and systems (AICAS)

作者： Jin Young Shin Sang Ho Lee Kwanghyun Go Soohee Kim Seung Eun Lee Dept. of Electronic Engineering Seoul National University of Science and Technology Seoul Republic of Korea

the usage of various sensors in vehicles has increased with the generalization of advanced driver assistance systems (ADAS). To ensure the safety of drivers and pedestrians, considering the accuracy of measured sensor data is essential. In this paper, we propose a data correction system for enhancing the accuracy of distance data from an ultrasonic sensor utilizing an AI processor. the proposed system detects the motion of an object and adjusts the obtained distance data to align with an ideal gradient of sequential data. Experimental results of the proposed system show an error detection rate of 90.6%.

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Nonlinear Partial Feedback Linearized Controller Design for Islanded AC Microgrid Connected distributed Generations 21

Nonlinear Partial Feedback Linearized Controller Design for ...

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21st ieee international conference on Environment and Electrical Engineering / 5th ieee Industrial and Commercial Power systems Europe (EEEIC/I and CPS Europe)

作者： Mahmud, Md Rasel Pota, Hemanshu Univ New South Wales Sch Engn & Informat Technol Canberra ACT Australia

ISBN: (纸本)9781665436137

this paper presents nonlinear partial feedback linearized control scheme for AC microgrid connected DC-AC voltage source converters. the feedback linearization approach partially linearizes the nonlinear dynamical model of the DC-AC converters. A standard PD controller is applied to the linearized model to regulate the output voltage of the distributed generations. the proposed controller performance is illustrated under two different scenarios: demand load change in common AC-bus, and another one is three-phase to ground short-circuit fault.

关键词： DC-AC Voltage Source Converter distributed Generations Partial feedback linearization AC Microgrids

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Accessing the Performance of K-Medoid, K-Means and FCM Clustering Techniques for Wireless sensor Networks

Accessing the Performance of K-Medoid, K-Means and FCM Clust...

引用

India Council international Subsections conference (INDISCON), ieee

作者： N. thiyagarajan N. Shanmugasundaram Department of Electronics and Communication Engineering Sri Eshwar College of Engineering Coimbatore Tamilnadu India

ISBN: (数字)9798350376753

ISBN: (纸本)9798350376760

the real-time data collection and monitoring process demands Wireless sensor Networks (WSNs) as a pivotal technology that employs randomly distributed battery-operated sensor modules to facilitate seamless data transmission from remote and often hostile environments. the versatility of WSNs extends across various domains, including monitoring of smart cities. By harnessing the power of WSNs, we can optimise processes, enhance productivity, and mitigate risks more effectively. Clustering is the widely discussed concrete part of the energy mitigating process. We wish to evaluate the clustering in WSN with the Fuzzy C-Means Clustering, K-medoid clustering, and K-means. the clustering process was analysed with the estimation of the silhouette score. In order to assess their performance, numerous simulations are conducted in the MATLAB environment. Furthermore, we discussed their mean silhouette score, clustering ability for different scenarios in WSN.

关键词： Productivity Wireless sensor networks Smart cities Estimation Routing Real-time systems Energy efficiency MATLAB Monitoring Optimization

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An Efficient CNN-LSTM Approach for Smartphone sensor-Based Human Activity Recognition System 5

An Efficient CNN-LSTM Approach for Smartphone Sensor-Based H...

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5th international conference on Computational Intelligence and Networks, CINE 2022

作者： Choudhury, Nurul Amin Soni, Badal National Institute of Technology Silchar Dept. of Computer Science & Engineering Assam Silchar788010 India

ISBN: (纸本)9781665464659

Human Activity Recognition - HAR is one of the most popular area in the filed of sensor technology and smart learning algorithms. Deep learning algorithms are immensely exploited in HAR systems as it eliminates the need of manual feature engineering. Researchers use normal and hybrid deep learning schemes for training and comparing the models. this paper proposes an efficient CNN-LSTM model for recognising daily human activities using smartphone sensor data. A contemporary CNN-LSTM model is created using time distributed feature extraction layers as it can efficiently handle hierarchical features and can selects the relevant features easily using LSTM memorization scheme. the proposed CNN-LSTM model is compared with two other models - DNN and LSTM in terms of accuracy, precision, recall, F1- score, training loss and computational times. the proposed model managed to outperform other models optimally in all the evaluation metrics. Using holdout training and testing split, the model managed to achieve an average accuracy of 97.609% and 98.69% with relu activation function and 100 training iteration. On validating the different models, the hybrid models takes less computational time and managed to achieve an computational efficiency of (76.23 ± 140.76)% from other models. © 2022 ieee.

关键词： Wearable sensors

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Proceedings - 2022 ieee international conference on Blockchain, Blockchain 2022

Proceedings - 2022 IEEE International Conference on Blockcha...

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5th ieee international conference on Blockchain, Blockchain 2022

ISBN: (纸本)9781665461047

the proceedings contain 70 papers. the topics discussed include: permissionless blockchain-based sybil-resistant self-sovereign identity utilizing attested execution secure processors;monitoring provenance of delegated personal data with blockchain;agent-based modelling of bitcoin consensus without block rewards;the Philos Trust algorithm: preventing exploitation of distributed trust;cryptocurrency price prediction with multi-task multi-step sequence-to-sequence modeling;analysis of Polkadot: architecture, internals, and contradictions;blockchain-based federated learning for industrial metaverses: incentive scheme with optimal Aoi;blockchain-based solutions for education credentialing system: comparison and implications for future development;Mosaic - a blockchain consensus algorithm based on random number generation;and a scalable blockchain-based smart contract model for decentralized voltage stability using Sharding technique.

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distributed Multi-Dimensional Data Index Strategy in Cloud computing Environment 5

Distributed Multi-Dimensional Data Index Strategy in Cloud C...

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5th international conference on Electronics, Communication and Aerospace Technology, ICECA 2021

作者： Gui, Dawei He, Guoqi School of Information and Intelligent Technology Shaanxi Radio and Television University Shaanxi Xian710119 China

ISBN: (纸本)9781665435246

the widespread application of cloud computing technology makes data show an explosive growth trend, and poses new challenges to traditional data management technology. Existing cloud storage systems generally use distributed hash tables to access data. this key-value-based model can achieve higher access efficiency in single-dimensional queries, but it does not support multi-dimensional queries. therefore, in recent years, cloud storage auxiliary indexing has become a hot topic in academic research, and related results have been published in top international conferences and top journals in the field of database. this paper studies the distributed multi-dimensional data index strategy under the cloud computing environment. the work of the thesis is carried out from two aspects: multi-dimensional data index in cloud storage and distributed computing. © 2021 ieee.

关键词： Cloud storage

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distributed subway station safety detection system based on Raspberry Pi 5

Distributed subway station safety detection system based on ...

引用

5th international conference on Data Science and Information Technology, DSIT 2022

作者： Wang, Yang Jiang, Shenggang Zeng, Zhao Xiong, Hanyue Fang, Qiubao Li, Yazhuo College of Intelligent Manufacturing Jianghan University Wuhan China

ISBN: (数字)9781665498685

ISBN: (纸本)9781665498685

To ensure the safety of subway and maintain a good operating environment is the primary task of station operating units. therefore, the environmental and passenger detection of the key locations in the subway station is necessary, to obtain real-time information such as the air quality status in the station and the density of passenger at the station hall layer and the platform layer. Station operation management department can adjust the station operation and emergency plan in real time according to the various data and information inside the station to improve the security of passengers and property in the station. the traditional detection system performs data processing on the server and has too high requirements for the server. the raspberry Pi as an embedded device have abilities of environment detection, data processing and information transmission on the local area. Multiple Raspberry Pi can be distributed at the key locations in the subway station, sending processed detection data to the server through wireless ways, which can greatly reduce the requirements of the server performance of the station monitoring system. In order to realize air quality detection and passenger detection in station, this paper designs a distributed detection system based on Raspberry Pi 4 platform. Real-time detection and upload of air quality parameters such as temperature, humidity, CO and smoke in station and passenger detection parameters such as passenger number and passenger flow density. the system has the characteristics of low cost, high accuracy and strong real-time. © 2022 ieee.

关键词： Smoke

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7th international conference on computing in Engineering and Technology, ICCET 2022

7th International Conference on Computing in Engineering and...

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7th international conference on computing in Engineering and Technology, ICCET 2022

ISBN: (纸本)9789811927188

the proceedings contain 70 papers presendted at a virtual meeting. the special focus in this conference is on computing in Engineering and Technology. the topics include: Credit Point Based Voluntary Work Analysis: An Integrated Model Based on Proper Waste Disposal, Fitness Contribution and Social Work Index;empathic Chatbot: Emotional Astuteness for Mental Health Well-Being;Service Placement in Edge computing with AI Based Techniques;state of the Art on Cloud-Information Flow Control;A Smart Energy Optimization and Collision Avoidance Routing Strategy for IoT systems in the WSN Domain;secure Data Aggregation Model for People Centric Sensing Networks;energy Efficient Routing Protocol for Multisink Wireless sensor Networks;attention Based Deep Learning Techniques for Question Classification in Question Answering systems;a Perceptive Node Transposition and Network Reformation in Wireless sensor Network;A Hybrid RSS/ToA Based Situational Awareness Technique for Indoor Massive MIMO Wireless System;sleeping Node Scheduling Method Based Redundant Node Energy Reduction in Wireless sensor Networks;cloud computing Based Workload Prediction Using Cluster Machine Learning Approach;spatiotemporal Based Power Control Scheme for D2D Communication in 5G IoT Networks with QoS Constraints;sentiment of Primary Features in Aspect Based Sentiment Analysis of Hindi Reviews;An Optimized Cloud-Based Solution for Small-Scale Non-IT Business Firms: An Experimental Analysis;Cyber-Attack Prevention Based on Modified LVQ in SDN Using Neural Networks;performance Analysis of Scalable Transactions in distributed Data Store;distributed Data Register Technology as the Main Component of Economic Decentralization;securing Blood Distribution systems Using Blockchain;SelectGoREATech: Selection of Goal-Oriented Requirements Elicitation and Analysis Techniques;preface.

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