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检索条件"任意字段=2nd International Conference on Parallel, Distributed, Grid and Cloud Computing for Engineering"

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cloud-Enabled Isolation Forest for Anomaly Detection in UAV-Based Power Line Inspection

Cloud-Enabled Isolation Forest for Anomaly Detection in UAV-...

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Networking and Communications (ICNWC), international conference on

作者： Jayabharathi Ramasamy E. Srividhya V. Vaidehi S. Vimaladevi N. Mohankumar S. Murugan Department of Computer Science and Engineering RMK College of Engineering and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science Vinayaka Mission's Kirupananda Variyar Arts and Science College Vinayaka Mission's Research Foundation (DU) Salem Tamil Nadu India Department of Commerce and Business Administration School of Management Veltech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering B.V. Raju Institute of Technology Telangana India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350365269

ISBN: (纸本)9798350365276

Unmanned Aerial Vehicles (UAVs) gather data efficiently for power line inspection. Anomaly detection is essential for power infrastructure dependability and security. It proposes a cloud-Enabled Isolation Forest (CEIF) method for UAV-based power line inspection. It improves the isolation forest algorithm's efficiency and scalability in cloud computing. It can process huge UAV inspection datasets by dispersing cloud computing. The technique, which effectively isolates anomalies, is applied to the cloud for fast power line inspection and anomaly identification. It describes the CEIF system's cloud service integration and distributed computing algorithm optimization. Real-world UAV-based power line inspection datasets show it can accurately detect abnormalities with low false-positive rates. It is scalable and robust for improving power infrastructure dependability and security. It allows cloud services to deploy real-world settings to implement different inspection scales.

关键词： cloud computing Scalability Forestry Inspection Autonomous aerial vehicles Sensors Security

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An Efficient distributed Framework for Secured Multimedia Content Using cloud Storage

An Efficient Distributed Framework for Secured Multimedia Co...

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Computer Technologies (ICCTech), international conference on

作者： Yunus Parvej Faniband Iskandar Ishak Fatimah Sidi Marzanah A. Jabar Deloria Shaniska Razin C. H. Yap Center for Communication & IT Research Research Institute King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia Department of Computer Science Faculty of Computer Science and Information Technology Universiti Putra Malaysia UPM Serdang Selangor Malaysia Department of Software Engineering Faculty of Computer Science and Information Technology Universiti Putra Malaysia UPM Serdang Selangor Malaysia

Applications that support multimedia data such as medical pictures and videos, video broadcasting, and secure video conferencing consume vast quantities of information that require large numbers of servers to provide services for the many client users who access them. cloud-based sensitive data becomes increasingly important for the respective users to be kept confidential without infringing confidentiality, integrity, and availability. We propose a framework based on the OpenStack cloud-computing platform for multimedia data security. A prototype framework with a helper tool EncSwift is implemented, which allows stringent data access control and applying the data-at-rest Over-Encryption approach. The implementation is evaluated for computation of cryptography operations, cost of communications, and update policy consequences for the framework. With OpenStack Swift's open source and modular design, our multimedia data management and management framework is well suited to protecting and managing clouds' multimedia with low computational costs as shown in the experimental results.

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An industrial equipment maintenance support system for networked collaborative evolution in the smart product service ecosystem

An industrial equipment maintenance support system for netwo...

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Digital Twins and parallel Intelligence (DTPI), IEEE international conference on

作者： Yu-Guang Bao Zhao-Hui Sun Xiao-Qiang Liao Yuan Chang Siqi Qiu Xinguo Ming School of Mechanical Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9781665492270

ISBN: (纸本)9781665492287

The traditional model of industrial equipment operation and maintenance has changed with the development of industrial internet technologies such as edge computing, cloud-edge collaboration, big data, artificial intelligence, digital twins, etc. Meanwhile, researchers are beginning to focus on the range of influence of equipment asset reliability, which is shifting from local performance to organizational goals. The networked collaborative evolution of equipment assets and value chain management is becoming the core value proposition of industrial internet platforms. From the perspective of system architecture, this paper discusses a novel industrial equipment maintenance support system based on the industrial internet platform, detailing the design of the resource and edge layer, cloud platform layer, big data governance and service layer, modeling and simulation layer, information model and collaboration layer, and organization, business, and service layer. The proposed system architecture and solution are expected to provide possible guidance for group-type enterprises and cross-industry service providers to help them innovate equipment asset management solutions based on the industrial internet platform.

关键词： cloud computing Collaboration Systems architecture Organizations Maintenance engineering Big Data Data models

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DESIGN And SIMULATION OF A SELF-SIMILAR TRAFFIC GENERATOR FOR cloud computing

DESIGN AND SIMULATION OF A SELF-SIMILAR TRAFFIC GENERATOR FO...

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2012 IEEE 2nd international conference on cloud computing and Intelligence Systems

作者： Zhong Zheng Yanming Chen Jiapeng Xiu Beijing University of Posts and Telecommunications China Mobile Group Design Institute Co. Ltd School of Software Engineering Beijing University of Posts and Telecommunications

With the popularization of cloud services, understanding and analysing the basic characteristics of cloud computing, using it is features to design and optimize cloud services becomes more and more urgent and important. In reality, cloud computing has some irregular features: it is widely individual distributed, its individual flow is uneven, and is widely used, which requires that we first create a cloud model for laboratory studies, and to disseminate to the physical research. The composition of the cloud of individuals are numerous and scattered, makes us unable to go in search of a model from the details of the place, but from the large-scale ,traffic aggregation always follow the law of a particular point of view, and the complex network is the most common is to follow self-similar traffic. Therefore, the establishment of self-similar traffic model analysis of cloud computing network flow model is appropriate, and universal. NS2 is the current ideal simulation of large-scale network traffic software;however, the NS2 simulation environment is not self-similar flow generator. On this basis, the NS2 simulation platform design, implement and validate a self-similar traffic model based on the pareto. Based on the basic cloud computing network platform above, the in-depth analysis of cloud computing traffic, is of great significance for the study regarding the cloud computing performance, management, protocol, and QOS.

关键词： cloud computing Self-similar modeling NS2 Flow generator

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Analysis of WiFi Spatio-Temporal Data for Organic Fingerprinting-based Indoor Positioning System

Analysis of WiFi Spatio-Temporal Data for Organic Fingerprin...

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Electronic and Electrical engineering and Intelligent System (ICE3IS), international conference on

作者： Abdul Halim Ismail Amirah Husna Mohd Hajazi Muhamad Safwan Muhamad Azmi Mohd Sani Mohamad Hashim Mohd Nasir Ayob Hassrizal Hassan Basri Faculty of Electrical Engineering Technology Intelligent Computing and System Informatics Research Group (ICSI) Universiti Malaysia Perlis (UniMAP) Universiti Malaysia Perlis (UniMAP)

ISBN: (纸本)9781665465427

The mobile robot navigation is the next huge topic after positioning utilizing fingerprinting-based Wireless Positioning System (WPS). Many of recent works does not discuss this topic yet since many open problems in positioning topic are not yet solved, for instance the issues on multi-devices heterogeneity, instability of WiFi signals, granularity problems in grid-based indoor environment and many others. However, we anticipate that both positioning and navigation works must run in parallel so that the succession are guaranteed. This paper describes the analysis of spatio-temporal data of the signal obtained from the WiFi Access Point. Initial results suggest that the difference between transmitter heights have an effect on the spatio-temporal data while the handover of maximum signal strengths is inherent when three WiFi APs are used.

关键词： Wireless communication Electrical engineering Navigation Transmitters Fingerprint recognition Handover Indoor environment

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A Study on Battery Storage Exploitation using Reinforcement Learning

A Study on Battery Storage Exploitation using Reinforcement ...

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Applied Artificial Intelligence and computing (ICAAIC), international conference on

作者： Patel Badari Narayana Geetha Mahalakshmi Yadi Reddy M B. Suresh Kumar R B R Prakash D Suganthi Dept Of Mechanical Engg Mahatma Gandhi Institute Of Technology (Mgit) Hyderabad EEE Easwari Engineering College Chennai Department of EEE Chaitanya Bharathi Institute of Technology Hyderabad Telangana Electrical and Electronics Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Department of Computer Science Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical And Technical Sciences Chennai

An integral part of the new smart grid, battery storage is becoming an increasingly common requirement. Batteries are considered as an attractive alternative for other distributed intelligent energy resources because of their speed in responding to events like changes in renewable power or grid interruptions. Various research studied exist to explain about making money out of this talent. Any solution must take into account both the fast-paced nature of electrical phenomena and the more gradual changes in the relevant power markets. An area of AI called reinforcement learning has demonstrated promising results in optimizing difficult tasks with high degrees of uncertainty. As a result, the need for first-of-its-kind smart buildings is growing in tandem with the complexity of decentralized power systems and the widespread use of renewable energy sources. The initial portion of the algorithm is based on deep learning using recurrent neural networks to deal with the unknowns of power pricing and load needs in the future. The other method uses reinforcement learning to determine the best time to charge or discharge a battery bank based on profit, load, and grid peak conditions. This research study presents a reinforcement learning approach for battery scheduling, which is considered as a crucial component to meet the consumer objectives. The framework relies on a customer making a multi-criteria choice in order to maximize local energy production from batteries during peak demand. This will save costs by reducing the amount of power used by the grid. The reinforcement learning system used to choose the best battery scheduling actions uses predictions of available wind power. Consumers' understanding of what to do to maximize battery life in a variety of time-dependent settings is expanded thanks to the built-in learning mechanism. The established framework enables smart consumers to acquire knowledge of the stochastic environment and apply it to the process of making de

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Network selection using fuzzy logic

Network selection using fuzzy logic

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international conference on Broadband Networks (BroadNets)

作者： S. Kher A.K. Somani R. Gupta Dependable Computing and Networking Laboratory Department of Electrical and Computer Engineering Iowa State University Ames IA USA

The peer-to-peer technology offers many advantages, but at the same time, it poses many novel challenges for the research community. Modern peer-to-peer systems are characterized by large scale, poor reliability, and extreme dynamism of the participating nodes, with a continuous flow of nodes joining and leaving the systems. Selection of an optimal network requires estimation of its rank using attributes such as storage, average load, cost, reliability etc. There are multiple networks, each vying for users business by providing them novel services. The user in turn has to decide which network best meets its service requirements and accordingly join a network. In this paper, we propose a model with two ranking schemes, one being network-specific and the other user-specific. The schemes use fuzzy logic to rank different networks based on their attributes. The difference between the two ranking schemes lie in the dynamism offered by them. The user-specific ranking criteria is flexible while the network-specific scheme uses a fixed criteria. The network-specific scheme helps in providing a structure to visualize the overall performance index of the networks. The user-specific scheme is adaptive in the sense that it caters to the specific needs of the users. The simulations performed by us show that the two schemes are light-weight, highly accurate and easily implementable

关键词： Fuzzy logic Peer to peer computing grid computing Computer networks distributed computing Image storage Laboratories Large-scale systems Computer network reliability Cost function

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Seagull-optimized Deep Convolutional Neural Network for Detecting distributed Denial of Service Attack in cloud computing

Seagull-optimized Deep Convolutional Neural Network for Dete...

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Self Sustainable Artificial Intelligence Systems (ICSSAS), international conference on

作者： Yogesh B. Sanap Pushpalata G. Aher Computer Science and Engineering Sandip University Nashik Sandip University Nashik Maharashtra

ISBN: (数字)9798350368413

ISBN: (纸本)9798350368420

Due to the enormous benefits and applications of cloud computing, there is a lot of interest from researchers on cloud computing. The most significant security risk associated with cloud computing is insider distributed denial of service (DDoS), which affects the entire deactivation of services due to its dispersed architecture and utter dependency on the internet for service supply. Machine learning and Deep learning have appeared as a potent tool in cyber security recently, and numerous attack detection methods have been put out in recent studies. Nevertheless, existing studies on attack detection encounter several challenges, including data imbalance, interpretability issues, inaccurate detection, overfitting, and time complexity issues. Therefore, in order to overcome these difficulties, a deep learning model, Seagull optimized Synthetic Minority Over-sampling Technique enabled Deep Convolutional Neural Network (SgO-SCNN) is developed in this research to detect DDoS attacks. The optimized SMOTE employed in the model addresses the imbalanced data issue and improves the model’s performance by effectively rebalancing the data. The DCNN classifier can detect the attacks more efficiently and reduces the computation of the model. The seagull optimization tunes the DCNN classifier’s parameters and improves the model’s performance in attack detection with fast convergence, which reduces the model’s time complexity. The experimental outcomes indicate that the developed $\mathrm{SgO}-\mathrm{SCNN}$ model outperforms the existing approaches by attaining enhanced values of accuracy 96.05%, F1-score 94.66%, precision 94.84%, and recall 94.48%.

关键词： cloud computing Accuracy Computational modeling Denial-of-service attack Data models Convolutional neural networks Computer crime Time complexity Optimization Overfitting

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Hierarchical Model of cloud & Client computing based on Multi-agent

Hierarchical Model of Cloud & Client Computing based on Mult...

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2012 2nd international conference on Advanced Materials and Information Technology Processing(AMITP 2012)

作者： Xu Xiao-long Cheng Chun-Ling Xiong Jing-Yi College of Computer Nanjing University of Posts and Telecommunications

In order to explore the potential ability of cloud computing utilizing resources further,a new model of cloud computing is proposed in this paper,which divides the cloud & client computing environment into several layers in accordance with the type of node and adopts agent to encapsulate the behaviors and resources of node to achieve more efficient resource sharing and collaboration *** model takes full account of a variety of potential resources available owned by terminal nodes and deploys a variety of tasks divided from a job in an orderly manner to the core nodes,the general server nodes and terminal nodes in order to achieve the goal of maximizing resource *** further assure tasks can be completed efficiently and reliably,a specific cloud-coins-based incentive mechanism is also proposed,which can stimulate terminal nodes contributing its resources and working together ***,in this paper we present how to construct the experimental system and application demonstration of the hierarchical model of cloud & client computing based on multi-agent for reference,which also shows that our model is effective and feasible.

关键词： distributed computing cloud computing Multi-agent Task allocation Incentive

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Rapid Calibration Method for Public Field of View in Binocular Stereo Vision Sensor

Rapid Calibration Method for Public Field of View in Binocul...

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Intelligent Perception and Computer Vision (CIPCV), international conference on

作者： Xiaoqi Cheng Xiangwen Weng Haishu Tan Xiaosong Li The school of mechatronic engineering and automation Foshan University Foshan China The school of physics and optoelectronic engineering Foshan University Foshan China

ISBN: (数字)9798350360097

ISBN: (纸本)9798350360103

In response to the problem of lacking accurate means to acquire and present the binocular public field of view, a rapid calibration method for public field of view in binocular stereo vision sensor is proposed. This method first completes the precise calibration of the internal and structural parameters of the binocular camera based on the Zhang’s calibration method. Secondly, based on the binocular structural parameters and camera depth of field range, spatial point cloud is discretely sampled and coordinated systemized. Next, combined with the camera perspective projection model, the projection coordinates of spatial discrete point clouds on the binocular camera are calculated. Finally, filter the visibility of spatial discrete point clouds based on the size of binocular image and depth of field range, and represent the calibration results of the binocular public field of view in the form of a 3D visual point cloud. In the experimental section, using calibration image data sets of parallel and general binocular cameras, the obtained public field of view visible point cloud was demonstrated in relation to the spatial point cloud of target feature points, verifying the effectiveness and generality of this method.

关键词： Point cloud compression Visualization Computer vision cloud computing Three-dimensional displays Accuracy Cameras

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