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A software trigger based synchronization for multipurpose distributed acquisition systems

A software trigger based synchronization for multipurpose di...

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2020 international conference on Innovation and Intelligence for Informatics, computing and Technologies, 3ICT 2020

作者： Jetti, Harsha Vardana Salicone, Simona Politecnico di Milano Dipartimento di Energia Milano Italy Milano Italy

ISBN: (纸本)9781728196732

Synchronization is one of the key aspects and a requirement for modern day technologies. It's a requirement for almost every sector including power, health, retail, navigation, banking. Synchronization is also important when using distributed acquisition systems. Synchronous acquisition is strictly needed in applications like Phasor Measurement Units (PMU) and wide area sensor networks. Since the data from the sensors are all collectively processed by a central unit, it is important that the data from all sensors is synchronized. Therefore, the concept of synchronized sampling when using distributed acquisition systems forms an interesting research area. There have been numerous research papers discussing different methods to do this. But, in most of them, the available technology is specific to a particular application. This paper shows an initial attempt to present a solution with a PTP driven software trigger that could potentially be used for multiple applications. © 2020 ieee.

关键词： Synchronization

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A framework based on genetic algorithms for distributed constraint optimization problems 3

A framework based on genetic algorithms for distributed cons...

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3rd international conference on Electron Device and Mechanical Engineering, ICEDME 2020

作者： Liu, Lizhen Chongqing University College of Computer Science Chongqing China

ISBN: (数字)9781728181455

ISBN: (纸本)9781728181455

Local search algorithms are widely applied in solving large-scale distributed Constraint Optimization Problems(DCOPs). In local search algorithms, each agent makes its decision based on values of its neighbours. Thus, the search space of agents are bound up with those values. Fortunately, Genetic Algorithm(GA) can direct a search process to a more promising search space and help the search process break up the bound of local states, so we propose a GA-based framework (LSGA) to enhance local search algorithms. In LSGA, a novel fitness function is proposed to help agents evaluate the quality of value assignments. Moreover, we propose a method to decide crossover positions in term of agent-communication and the topological structure of DCOPs. Finally, we design a series of genetic operators for autonomous agents in DCOPs. The LSGA framework can be easily applied in any local search algorithm, and the experimental results demonstrate the superiority of the use of LSGA in the typical search algorithms over state-of-art local search algorithms. © 2020 ieee.

关键词： Multi agent systems

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Towards Reliable IoT: Fog-Based AI sensor Validation 3

Towards Reliable IoT: Fog-Based AI Sensor Validation

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3rd ieee international conference on Cloud and Fog computing Technologies and Applications (ieee Cloud Summit)

作者： Russell, Luke Goubran, Rafik Kwamena, Felix Carleton Univ Syst & Comp Engn Ottawa ON Canada

ISBN: (数字)9781728131016

ISBN: (纸本)9781728131016

Trust, reliability, and validation of data collected in distributed edge sensor systems is an increasingly relevant issue. Though the obvious solution of deploying redundant identical systems can provide validation, real-world modification constraints can sometimes make this difficult, or even prevent this. However, many distributed sensors exist for other purposes, that may be available to be used. Introducing validation with existing sensors may impose too high a requirement for bandwidth to use cloud-based validation, while edge-based validation may require too much computing power. A fog-based validation layer using sensory substitution is presented. With the rise of cyber-physical attacks on cloud, fog, and edge computing systems, validation is important, and lack of correct validation has been seen in some high impact cases where incorrect sensor data can be thought of as as true. A playback cyber-attack is discussed, and an algorithm for increasing reliability of IoT systems in the case of typical sensor errors or more serious incidents like cyber-physical attacks is presented. Given the need for dependable autonomy and reliability in IoT systems, this paper presents a method of sensor validation to increase robustness, resilience and dependability of sensed data by detecting false positives and negatives, and corroboration of true positives and negatives, using sensory substitution. Perhaps sometimes sensor data is trusted without ongoing validation. Using the example of cameras and artificial intelligence-based human presence detection, as well as using ambient distributed magnetometers and luminosity sensors, examples of a fog-based corroboration and validation methodology for human detection is presented. Results show the technique is an effective vector for sensor validation using available sensors, and scenarios where sensory substitution corrects false positives and false negatives from an artificial intelligence visual model are shown.

关键词： agile IoT sensing security validation cyber security cyber-physical systems

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A Study on the Application of One Dimension Convolutional Neural Network for Classification of Gestures from Surface Electromyography Data

A Study on the Application of One Dimension Convolutional Ne...

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ieee distributed computing, VLSI, Electrical Circuits and Robotics (DISCOVER)

作者： Praahas Amin Airani Mohammad Khan Mangalore University Mangalore India

ISBN: (纸本)9781665429528

Myoelectric control systems are gaining popularity with the availability of commercial, low-cost, surface electromyography sensors. These systems can be used for gesture recognition which finds application in human-machine interfaces. The gestures are recognized using pattern recognition algorithms. Machine learning or deep learning techniques can be applied for the classification of gestures. In this paper, a user-specific 1-Dimensional Convolution Neural Network is proposed for the classification of Surface Electromyography data recorded using a commercially available surface electromyography recording device to perform offline classification of 5 hand gestures using limited data of less than 400 samples. An average accuracy of 82%±3% was achieved during the study after cross-validation of the data using 5-fold stratified cross-validation.

关键词： Performance evaluation Machine learning algorithms distributed databases Gesture recognition Very large scale integration Electromyography sensor systems

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A New Optimal Control with Adjustable Gains for Nonlinear systems

A New Optimal Control with Adjustable Gains for Nonlinear Sy...

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3rd international conference on Intelligent Autonomous systems (ICoIAS)

作者： Do Xuan Phu Vietnamese German Univ Dept Mechatron & Sensor Syst Technol Binh Duong Vietnam

ISBN: (数字)9781728160788

ISBN: (纸本)9781728160788

In this study, a new optimal control law associated with the sliding mode control is developed on the basis of the Bolza-Meyer criterion. The salient characteristics of the controller proposed in this work is to have adjustable gains. This leads to the enhancement of control performances with the given cost function. After formulating the proposed optimal control law for nonlinear systems, two examples are simulated to validate some benefits of the proposed approach. It is demonstrated that the controller proposed in this work requires less power, but provides more stronger stability than conventional optimal controller.

关键词： Optimal control Optimal sliding mode control Bolza-Meyer criterion Cost function Hamiltonian function

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A Support Tool for Rapid Deployment of Large Scale distributed Sensing systems 2020

A Support Tool for Rapid Deployment of Large Scale Distribut...

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3rd international conference on Electronics, Communications and Control Engineering, ICECC 2020

作者： Truong, Tuyen Phong College of Engineering Technology Can Tho University Can Tho City Viet Nam

ISBN: (纸本)9781450374996

distributed sensing systems are spatially distributed autonomous sensing devices for environmental observation purposes in large areas. In recent years, the innovation of embedded sensing devices and wireless communication have been developed rapidly. It allows for improving the capability of processing and reducing energy consumption for a long time, wireless sensor networks (WSNs) in order to meet a wide range of applications. Besides wide-range coverage, LoRa technology also provides the benefits of low power consumption, scalability and interference immunity for wireless links. This paper proposes a support tool for the rapid deployment of large scale distributed sensing systems based on LoRa technology. The major motivation behinds this work is to help designer select the proper set of parameters for improving the performance of LoRa systems taking into account the strong impact of geographic topology. The experiments on a star LoRa network for environmental data dissemination was done to evaluate the effectiveness of the tool on the reduction of packet transmission time, known as time-on-air (ToA). © 2020 ACM.

关键词： Wireless sensor networks

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Artificial neural network model of an ieee 5 bus system under transient conditions 3

Artificial neural network model of an IEEE 5 bus system unde...

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3rd international conference on Smart systems and Inventive Technology, ICSSIT 2020

作者： Mohan, Archana Vidya, M.S. College of Engineering Trivandrum Department of Electrical and Electronics Engineering Trivandrum India

ISBN: (纸本)9781728158211

The distributed nature with increased complexity of modern power system is creating a new wave of exploration in the field of power system operations and management. Stability is said to be the most considering factor for the power system operation in this scenario. Transient stability is the ability of the power system to regain to its normal operation when subjected to physical disturbances is a vital problem. Nowadays transient modeling is making more significance for the fault identification and clearance in the networked power system. This helps to observe and study the pattern of changes from the stable to unstable operation on the basis of time as a criterion. This makes the system management more easier. The observations are then required to train a system by using an artificial topology for better performance and computation purpose. A transient behavior of a system with its steady state performance is captured and with the captured data, a model is generated based on the artificial neural network learning technology. The system is trained with two different algorithms and observed the comparison of the generated system. The simulation is carried out in Matlab / Simulink. © 2020 ieee.

关键词： Neural networks

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3rd Workshop on Education for High Performance computing (EduHiPC - 2021)

3rd Workshop on Education for High Performance Computing (Ed...

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ieee international conference on High Performance computing Workshops (HiPCW)

作者： Sheikh Ghafoor Sushil K. Prasad

ISBN: (纸本)9781665410380

High Performance computing (HPC) and, in general, Parallel and distributed computing (PDC) is ubiquitous. Every computing device, from a smartphone to a supercomputer, relies on parallel processing. Compute clusters of multicore and manycore processors (CPUs and GPUs) are routinely used in many subdomains of computer science, such as data science, parallel machine learning and high performance computing. Therefore, it is important for every computing professional (and especially every programmer) to understand how parallelism and distributed computing affect problem solving. It is essential for educators to impart a range of PDC and HPC skills and knowledge at multiple levels within the curriculum of Computer Science (CS), Computer Engineering (CE), and related disciplines such as computational data science. Software industry and research laboratories require people with these skills, more so now. Therefore, they now engage in extensive on-the-job training. Additionally, rapid changes in hardware platforms, languages, and programming environments increasingly challenge educators to decide what to teach and how to teach it, in order to prepare students for careers that are increasingly likely to involve PDC and HPC. EduHiPC aims to provide a forum that brings together academia, industry, government, and non-profit organizations - especially from India, its vicinity, and Asia - for exploring and exchanging experiences and ideas about the inclusion of high-performance, parallel, and distributed computing into undergraduate and graduate curriculum of Computer Science, Computer Engineering, Computational Science, Computational Engineering, and computational courses for STEM and business and other non-STEM disciplines.

关键词： High Performance computing distributed computing PDC gene computer science Curriculum Nonprofit Organizations Education educators supercomputer Smartphone COMPUTER ENGINEERING Laboratory Research Multi-core processors HIGH-PERFORMANCE career

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Tightly-coupled Vision-Gyro-Wheel Odometry for Ground Vehicle with Online Extrinsic Calibration 3

Tightly-coupled Vision-Gyro-Wheel Odometry for Ground Vehicl...

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3rd international conference on Intelligent Autonomous systems (ICoIAS)

作者： He, Yixuan Chai, Zheng Liu, Xiao Li, Zhaohui Luo, Haiyong Zhao, Fang Beijing Univ Posts & Telecommun Sch Software Beijing Peoples R China Megvii Co R SLAM Beijing Peoples R China Chinese Acad Sci Inst Comp Technol Beijing Peoples R China

ISBN: (数字)9781728160788

ISBN: (纸本)9781728160788

In the absence of GPS or other sensors that provide absolute pose, it is relatively difficult to estimate the accurate pose of the ground vehicles continuously. Estimation method combining visual measurement and inertial data (VINS) can achieve high accuracy, and gradually attracts the attention of researchers. However, It has been proved that VINS has additional unobservable direction, scale, under constant speed or acceleration. For ground vehicle, these motions can often occur, which has a negative impact on the localization performance. Futhermore, accelerometer measurements are greatly affected by noise and the good bias estimation of accelerometer is needed for an accurate integration. In this paper, we present a method fusing the measurements from a stereo camera, gyro and wheel odometer. Compared to accelerometer, wheel odometer can provide more reliable translation information. Furthermore, replacing accelerometer with wheel odometer overcomes the additional unobservability of scale. And we also prove that, in online optimization, the z-axis of the translation of extrinsic parameters between camera and wheel odometry is unobservable under planar motion. Hence, the additional constraint is needed for this. By the real-world experiments in industrial indoor environments, we validate the better performance of the proposed system compared to other state-of-the-art approach.

关键词： Simultaneous localization and mapping sensor fusion wheel encoder observability

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Evaluation of Classification algorithms for distributed Denial of Service Attack Detection 3

Evaluation of Classification algorithms for Distributed Deni...

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3rd ieee international conference on Artificial Intelligence and Knowledge Engineering AIKE 2020

作者： Gohil, Maulik Kumar, Sathish Cleveland State Univ Elect Engn & Comp Sci Dept Cleveland OH 44115 USA

ISBN: (纸本)9781728187082

distributed Denial of Service (DDoS) attacks aims exhausting the target network with malicious traffic, which is a threat to the availability of the service. Many detection systems, specifically Intrusion Detection System (IDS) have been proposed throughout the last two decades as the Internet evolved, although users and organizations find it continuously challenging and defeated while dealing with DDoS. Though, IDS is the first point of defense for protecting critical networks against ever evolving issues of intrusive activities, however it should be up to date all the time to detect any anomalous behavior so that integrity, confidentiality and availability of the service can be preserved. But, the accuracy of new detection methods, techniques, algorithms heavily rely on the existence of well-designed datasets for training purposes and evaluation by creating the classifier model. In this work, experimentation has been carried out using major supervised classification algorithms to classify the DDoS attack accurately from the legitimate flows. Among all the classifier, tree-based classifiers and distance-based classifiers performed the best.

关键词： Machine Learning DDoS Logistic Regression Naive Bayes SVM Decision Tree Random Forest K-NN

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