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Network Migration and Performance Analysis of IPv4 and IPv6

Network Migration and Performance Analysis of IPv4 and IPv6

引用

2020 international conference on Computer, control, electrical, and Electronics engineering, ICCCEEE 2020

作者： Ahmed, Mofti Rafie AbdelGhani Shaikhedris, Solafa Salahaldin Ali Sudan International University Faculty of Computing and Information Systems Khartoum Sudan Sudan University of Science and Technology College of Computer Science and Information Technology Khartoum Sudan

ISBN: (纸本)9781728191119

Protocols represent an essential role in today's communication world, where it is not possible for one computer to communicate with another. Internet Protocol Version 4 (IPv4) is the first generation of internet protocols, which consists of 32bit. However, with the passage of time and with frequent use of the internet the existing addresses has been finished, and this lead to the looking for new IPs. A new network layer protocol has been proposed named Internet Protocol Version 6 (IPv6). This paper focus on internet Protocol version 6 and it's transition or migration techniques then will make performance analysis of ipv4 and ipv6 for data and voice traffics under using riverbed. Through this paper, we proved the dual stack is the best technique for migration from ipv4 to ipv6 and then the tunnel technique is better technique after dual stack. In addition, we proved the pure ipv6 network and dual stack have high performance than ipv4 network. © 2021 IEEE.

关键词： Network layers

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Object Detection in Self Driving Cars Using Deep Learning

Object Detection in Self Driving Cars Using Deep Learning

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2021 IEEE international conference on Innovative computing, Intelligent Communication and Smart electrical Systems, ICSES 2021

作者： Prajwal, P. Prajwal, D. Harish, D.H. Gajanana, R. Jayasri, B.S. Lokesh, S. National Institute of Engineering Department of Computer Science and Engineering Mysuru India

ISBN: (纸本)9781665435215

In the Computer Vision domain, there has been continuous growth and development with main focus so as to facilitate a smooth interaction between Machines and human. Perception, planning and control are the main aspects that make up the Self-driving system. Perception subsystem converts the raw data collected by sensors or other information capturing devices into a model of the environment surrounding us. Planning subsystem analyses this model of the surrounding environment and makes certain purposeful decisions based on the inferences obtained from the analysis. Finally, the control Subsystem is responsible for execution of the actions or the decisions planned previously. The scope of this project is to study and analyze the problems faced in the Perception subsystem in the domain of detecting objects for autonomous cars. Previously, technologies like Radar, LiDAR, GPS and various other sensors had been employed for Driverless cars for mapping the surroundings of the car. However, in the recent past, some deep neural network (DNN) architectures like YOLO (You Only Look Once), and SSD (Single Shot MultiBox Detector) have been developed which are capable of detecting objects even when live video is considered as the input, thus having potential to be included as a part of the Driverless car systems. Selection of a model having considerable accuracy and producing results at a faster rate is very much essential so as to meet the requirements of object detection in driverless cars. In this project, we have used Caffe, which is developed by Berkeley AI Research and Community contributors as the deep learning framework. Keeping in mind the factors that contribute to the selection of a good model, we have chosen SSD model along-side MobileNet Neural network as the base architecture as it results in both faster rate of result production and has a moderate accuracy. © 2021 IEEE.

关键词： Computer vision

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Identification and Classification of Electronic Devices Using Harmonic Radar

Identification and Classification of Electronic Devices Usin...

引用

international conference on Distributed computing in Sensor Systems (DCOSS)

作者： Beatrice Perez Timothy J. Pierson Gregory Mazzaro David Kotz Department of Computer Science Dartmouth College Hanover NH USA Department of Electrical & Computer Engineering The Citadel The Military College of South Carolina Charleston SC USA

Smart home electronic devices invisibly collect, process, and exchange information with each other and with remote services, often without a home occupants' knowledge or consent. These devices may be mobile or fixed and may have wireless or wired network connections. Detecting and identifying all devices present in a home is a necessary first step to control the flow of data, but there exists no universal mechanism to detect and identify all electronic devices in a space. In this paper we present ICED (Identification and Classification of Electronic Devices), a system that can (i) identify devices from a known set of devices, and (ii) detect the presence of previously unseen devices. ICED, based on harmonic radar technology, collects measurements at the first harmonic of the radar's transmit frequency. We find that the harmonic response contains enough information to infer the type of device. It works when the device has no wireless network interface, is powered off, or attempts to evade detection. We evaluate performance on a collection of 17 devices and find that by transmitting a range of frequencies we correctly identify known devices with 97.6% accuracy and identify previously unseen devices as ‘unknown’ with 69.0% balanced accuracy.

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Conservative level controllable robust economic dispatch considering wind power

Conservative level controllable robust economic dispatch con...

引用

electrical engineering and control science (IC2ECS), international conference on

作者： Danwen Yu Qingqing Zhang Song Gao State Grid Shandong Electronic Power Research Institute Shandong China

This paper discusses the form of uncertainty set commonly used in robust optimization and the control method of conservatism. Furthermore, the definition and linearization method of the value-at-risk index of wind power acceptance conditions are given, and the boundary between disturbance range and system risk acceptance ability is realized. Linear association. On this basis, a robust real-time dispatching model is constructed that takes into account the probabilistic characteristics of wind power, aiming at minimizing the operating cost and the risk cost of node wind power acceptance. make decisions. In the process of model solving, the risk cost function of the node receiving wind power is linearized, and at the same time, the robust peer-to-peer model is obtained by using the linear transformation process of robust optimization. Among them, the AGC unit adopts the affine mechanism to participate in the smoothing process of wind power disturbance, which improves the solution efficiency and practicability of the model.

关键词： Uncertainty Costs Smoothing methods Stochastic processes automatic generation control Wind power generation Real-time systems

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Ultrasonic sensor enabled smart automated railway gate control system

Ultrasonic sensor enabled smart automated railway gate contr...

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2021 international conference on computing, Communication, electrical and Biomedical Systems, ICCCEBS 2021

作者： Senthil, Kumar R. Prabu, M. Rani, K.S. Prabaakaran, K. Chowdary, Penekalapati Soniya Rithika, K. Department of Electrical and Electronics Engineering Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai India Department of Electrical and Electronics Engineering Jaya College of Engineering and Technology Chennai India

Railways is the most preferred modes of transport system for freights as well as passengers. Among the accidents occurring in railways, the accident due to unmanned railway crossing are predominant. This article focuses in providing a smart automatic solution for vigilant monitoring in avoiding the accidents in railway crossing, system is provided with extra features to perform superior to raw conventional automatic railway gate, instead of blind closing of gates, gates are provided with the sensor which detect any persons or vehicle beneath it when it is operated. Along gate, pop up projection are recommended in this system which will be effectively restricting the movement across railway crossing during the operation of gates. Additionally, a monitoring system is provided which gives signal to train drivers if any vehicle/living being got struck in between the railway gate, well in advance which aids train drivers to act accordingly. Along with all these additional features conventional alarm feature is also available to alert. The prototype designed with the above feature effectively gives a solution in avoiding accidents by strongly enforce the restriction in movement across the gates when train crossing the unmanned railway crossing, reaction time of the monitoring system in giving alert to train driver, if any vehicle/living being got locked between the gates is very low. © Published under licence by IOP Publishing Ltd.

关键词： Railroad accidents

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ICICOS: Industrial Cyber Intelligent control Operating System for Cloud and Edge computing 5

ICICOS: Industrial Cyber Intelligent Control Operating Syste...

引用

5th IEEE international conference on Smart Internet of Things, SmartIoT 2021

作者： Su, Weibin Kong, Yanchun He, Zhengfang Edge Computing and Network Center Yunnan Technology and Business University Kunming China Faculty of Construction Engineering Kunming Metallurgy College Kunming China

ISBN: (纸本)9781665445115

We introduce ICICOS, a specific cloud and edge computing platform that provides industrial automation and artificial intelligence services. This platform is based on IEC61131-3 programming standard that used to replace traditional Programmable Logic controller (PLC). As is known to all, PLC has been playing an important role during the years of industrial automation revolution, we call it the Third Industrial Revolution. But the limited computing power of this device is not able to support artificial intelligence well, which cannot support deep learning algorithms. While different variations and implementations of controllers have been created over the last decade, ICICOS focuses on virtualization technology of industrial automation. It brings many benefits, including: the standardized electrical programming language brings a lot of users;the backup redundancy of virtual technology enhances the stability of the system;the concentration of computing power makes it competent for reasoning and learning of artificial intelligence. © 2021 IEEE.

关键词： Deep learning

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Current Averaging control Method in Low Duty Cycle Range for Dual Capacitor Output Three Inductors Interleaved High-Gain Converter

Current Averaging Control Method in Low Duty Cycle Range for...

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international conference on Power System Technology (POWERCON)

作者： Yingying Li Zhiquan Wu Xin Zhang Lin Zhu Fan Wang Yubin Wang State Power Investment Corporation Research Institute Co. Ltd Beijing China SPIC Yunnan International Power Investment Co. Ltd Kunming China Yunnan Power Investment Green Energy Technology Co. Ltd SPIC Yunnan International Power Investment Co. Ltd Kunming China School of Electrical Engineering Shandong University Jinan China

The dual capacitor output three inductor interleaved high-gain converter has broad application potential in many high gain applications. The converter input side adopts three-phase interleaved technique, the output side adopts three-level technique, effectively reducing the switching stresses of the converter. Besides having a higher voltage conversion capability compared to traditional Boost converters, this converter also has the characteristics of automatic inductor currents averaging and capacitor voltages stabilizing. However, this converter has the common shortcoming of high-gain DC-DC converters using capacitor energy storage step-up technique, which is that the duty cycle operating range is narrow. When the working duty cycle range of the converter is low range, that is, from 0 to 0.5, the converter's inductor currents can't be evenly divided. To address the above issue, a current averaging control method in low duty cycle range for the dual capacitor output three inductor interleaved high-gain converter is proposed. This method can make the converter have the characteristics of automatic inductor currents averaging and capacitor voltages stabilizing in a low duty cycle range, which is 0 to 0.5, for the dual capacitor output three inductor interleaved high-gain converter. A detailed analysis using the above control methods is studied on the circuit mode, voltage gain, automatic current averaging and voltage stabilizing characteristics, and switching stresses of the converter. Finally, the superiority of the proposed current averaging control method in low duty cycle range was verified by converter prototype experiments compared with traditional control methods.

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Autonomous Vehicle Health Monitoring Based on Cloud-Fog computing

Autonomous Vehicle Health Monitoring Based on Cloud-Fog Comp...

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Emerging Trends in electrical, control, and Telecommunication engineering (ETECTE), international conference on

作者： Nida Jabeen Runfang Hao Ashfaq Niaz Muhammad Usman Shoukat Fahim Niaz Mehran Arshad Khan College of Information and Computer Taiyuan University of Technology Taiyuan China College of Electrical and Power Engineering Taiyaun University of Technology Taiyuan China School of Automotive Engineering Wuhan University of Technology Wuhan China Department of Computer Science Islamia College Peshawar Peshawar Pakistan Department of Computer Science Government College University Faisalabad Pakistan

ISBN: (纸本)9781665492430

Almost every part of the economy relies on the transportation industry. The road transportation sector is one of the numerous types of transport sectors. As a vehicle's continual use causes damage, inefficiency, and faults in numerous parts, etc. Timely maintenance prevents or fixes these issues. Aiming at the requirements of vehicle health monitoring, this article studies and designs the cloud computing collaborative vehicle health monitoring network system and proposes the allocation of data transmission paths with an ant colony algorithm as the core. On this basis, it studies the construction of autonomous vehicle health status detection systems based on cloud and fog collaborative computing and gives the system level composition and system framework scheme. In the system, data is collected by sensor equipment and then transmitted, stored, and comprehensively processed. The obtained data provides a technical basis for automobile health detection. Through the construction of a simulation environment, it shows that the algorithm can adjust to the dynamic features of cloud system networks in the environment of road strength construction. The ant colony optimization (ACO) algorithm is a type of probabilistic algorithm that determines the best route through a graph. As compared with the shortest path first (SPF) algorithm and the brute force (BF) storm algorithm in terms of average throughput and response time, it is more suitable for cloud and fog collaborative network transmission environments.

关键词： Cloud computing Federated learning Heuristic algorithms Transportation industry Collaboration Throughput Automobiles

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A Gated Recurrent Unit based Intrusion Detection for SCADA Networks 6

A Gated Recurrent Unit based Intrusion Detection for SCADA N...

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6th international conference on computing, Communication and Security, ICCCS 2021

作者： Kasongo, Mambwe Sydney Sun, Yanxia University of Johannesburg Electrical and Electronic Engineering Science Johannesburg South Africa

ISBN: (纸本)9781665440394

Industrial control systems are rapidly evolving and they process large volumes of critical information that flow through them. Moreover, the development and advances of various industrial communication systems and the ascent of Internet connectivity have caused a surge in new types of threats and intrusions. In this study we implement a Gated Recurrent Unit (GRU) Intrusion Detection System (IDS) destined to secure Supervisory control and Data Acquisition (SCADA) networks. The GRU algorithm used in this research is coupled to the Information Gain (IG) approach for feature selection. The NSL-KDD dataset was employed to assess the performance of the IG-GRU-IDS. The results demonstrated that with only 20 attributes of the NSL-KDD, the IG-GRU-IDS achieved a validation accuracy of 99.52%, a test accuracy of 87.49% and a F-measure of 99.51 %. These results were superior to those obtained by Simple RNNs and LSTM based RNNs. © 2021 IEEE.

关键词： Network security

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Learnable Position-based Joint Entity-Relation Extraction Filtering Model

Learnable Position-based Joint Entity-Relation Extraction Fi...

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computing, Communication, Perception and Quantum Technology (CCPQT), international conference on

作者： Jingjing Liu Juan Zhang Ming Wu School of Electronic and Electrical Engineering Shanghai University of Engineering Science Shanghai China

The goal of entity relation joint extraction is to simultaneously extract entities and their relationships from text, representing this information in a structured manner, typically presented in the form of triples (entity1, relationship, entity2). This task holds significant importance in natural language processing applications such as knowledge graph construction and expansion, automatic question answering, information retrieval, and text mining. However, existing relation extraction methods still face several challenges. The core idea behind Seq2Seq models is to transform input sequences into output sequences, typically consisting of an encoder and a decoder. While Seq2Seq models excel in multiple tasks, they still encounter certain issues. For instance, they may be limited in dealing with challenges like redundancy, long-distance dependencies, and polysemy. To address these challenges, this study introduces an innovative approach. Firstly, we incorporate a "filter layer" inspired by signal processing to alleviate the decline in extraction performance caused by data redundancy. Secondly, we adopt variable-length positional encoding techniques to effectively capture word positions in sequences, enhancing the model's understanding of text. Additionally, we introduce dense connections, successfully mitigating the vanishing gradient problem in deep learning networks, thereby improving information flow and performance. Through these innovative methods, our model demonstrates significant performance improvements on two benchmark datasets, affirming its effectiveness and practicality in entity relation joint extraction tasks. These contributions are expected to drive advancements in the field of relation extraction, enhance the effectiveness of natural language processing tasks and hold significant implications for knowledge graph construction and expansion, among other domains.

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