scholarly journals Fishery Data Distribution System Based on Distance Prior Network Coding Strategy with Buffer Mapping Mechanism

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Wei Zhang ◽  
Xiarui Li ◽  
Renjie Zhou ◽  
Yangyang Bai
Keyword(s):  
Network Coding ◽  
Distribution System ◽  
Data Distribution ◽  
Block Size ◽  
Original Data ◽  
Experimental Results ◽  
Distribution Model ◽  
Time Data ◽  
Fishing Vessels ◽  
Distribution Efficiency

Marine fishery production safety has always been a livelihood issue of high concern to government departments at all levels in China. With the construction of marine informatization, a large number of fishing vessels have entered the Vessel Monitoring System (VMS). The massive, highly concurrent, and continuous track data generated by these vessels has posed great challenges to real-time data distribution. To cope with low distribution efficiency in the original data distribution system, this paper proposed a data distribution model based on the combination of network coding and UDP, which improves both reliability and efficiency of data distribution. In order to further enhance the efficiency of data distribution, a Codeword Distance Priority Protocol based on Buffer Map (CDPPBM) was added to the proposed model for the concentration of innovative codewords in the network, thereby increasing the effectiveness of data received by nodes. Experimental results show that the protocol proposed in this paper improves the data distribution efficiency by about 75% on average, compared with the LT code. The influence of data block size on network coded data distribution system is not involved in the previous work. Therefore, this paper discusses the Block to Piece Protocol (BPP) for large files, divides large files into fixed sizes equally, and distributes n data blocks multiple times to find the optimal piece size. The experimental results show that there is an optimal n for large files of different sizes, which can maximize the efficiency of data distribution.

A Bayesian Estimation of Reliability Measures of Stepwise Exponential Distribution System

1991 ◽  
Author(s):  
M. H. Hu
Keyword(s):  
Bayesian Estimation ◽  
Exponential Distribution ◽  
Distribution System ◽  
Failure Process ◽  
Reliability Function ◽  
Distribution Model ◽  
Time To Failure ◽  
Reliability Measures ◽  
In Series ◽  
Mean Time

Abstract This paper presents an analysis method for reliability measures of a system with step changes in failure and repair rates. Both failure and repair time have exponential function of time. Such a system is called a stepwise exponential distribution system. This kind of failure process can take place in various equipments. This paper deals with the system having components in series arrangement. Bayesian statistics is used in defining prior and posterior probability density functions of failure and repair rates. These functions provide information for the estimation of reliability measures: 1) failure and repair rates, 2) mean time to failure, 3) mean time to repair, 4) reliability function and 5) availability. A sample problem is given to illustrate the methodology. The Bayesian estimation of the stepwise exponential distribution model is useful in the planning of equipment predictive maintenance.

DIAL: A Web-Based Interoperable Scientific Data Distribution System

1999 ◽  
pp. 301-312 ◽  
Author(s):  
Liping Di ◽  
R. Suresh ◽  
K. Doan ◽  
Doug Ilg ◽  
Ken McDonald
Keyword(s):  
Distribution System ◽  
Data Distribution ◽  
Scientific Data ◽  
Web Based

scholarly journals DC Microgrid Utilizing Artificial Intelligence and Phasor Measurement Unit Assisted Inverter

Energies ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 6086
Author(s):  
Raziq Yaqub ◽  
Mohamed Ali ◽  
Hassan Ali
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Distribution System ◽  
Reference Signal ◽  
Phasor Measurement Unit ◽  
Measurement Unit ◽  
Time Data ◽  
Dc Microgrid ◽  
Utility Company ◽  
Phasor Measurement

Community microgrids are set to change the landscape of future energy markets. The technology is being deployed in many cities around the globe. However, a wide-scale deployment faces three major issues: initial synchronization of microgrids with the utility grids, slip management during its operation, and mitigation of distortions produced by the inverter. This paper proposes a Phasor Measurement Unit (PMU) Assisted Inverter (PAI) that addresses these three issues in a single solution. The proposed PAI continually receives real-time data from a Phasor Measurement Unit installed in the distribution system of a utility company and keeps constructing a real-time reference signal for the inverter. To validate the concept, a unique intelligent DC microgrid architecture that employs the proposed Phasor Measurement Unit (PMU) Assisted Inverter (PAI) is also presented, alongside the cloud-based Artificial Intelligence (AI), which harnesses energy from community shared resources, such as batteries and the community’s rooftop solar resources. The results show that the proposed system produces quality output and is 98.5% efficient.

scholarly journals An adaptive data distribution system for mobile environments

1996 ◽  
pp. 159-166 ◽  
Author(s):  
Sascha Kümmel ◽  
Alexander Schill ◽  
Karsten Schumann ◽  
Thomas Ziegert
Keyword(s):  
Distribution System ◽  
Data Distribution ◽  
Mobile Environments

Dynamic Grid-Based Algorithms in DDM

2012 ◽  
Vol 433-440 ◽  
pp. 4297-4301
Author(s):  
Hui Ru Wang ◽  
Jing Ding
Keyword(s):  
Large Scale ◽  
Data Distribution ◽  
Experimental Results ◽  
Interactive Simulation ◽  
Distribution Management ◽  
Distributed Interactive Simulation ◽  
Dynamic Multicast ◽  
Grid Based

For large-scale distributed interactive simulation, it is important and difficult for data to communicate among thousands of objects. The purpose of the Data Distribution Management (DDM) service performs data filter and reduces irrelevant data between federations. Grid-based algorithm can only manage to filter part of irrelevant data. Experimental results show that, compare with normal grid-based algorithms, the dynamic multicast method can minimize.

scholarly journals Distributionally Adversarial Attack

2019 ◽  
Vol 33 ◽  
pp. 2253-2260 ◽  
Author(s):  
Tianhang Zheng ◽  
Changyou Chen ◽  
Kui Ren
Keyword(s):  
Data Distribution ◽  
Original Data ◽  
First Order ◽  
Risk Optimization ◽  
Wide Range ◽  
Data Points ◽  
Adversarial Attack ◽  
Projected Gradient Descent ◽  
Original Objective ◽  
Direct Dependency

Recent work on adversarial attack has shown that Projected Gradient Descent (PGD) Adversary is a universal first-order adversary, and the classifier adversarially trained by PGD is robust against a wide range of first-order attacks. It is worth noting that the original objective of an attack/defense model relies on a data distribution p(x), typically in the form of risk maximization/minimization, e.g., max/min Ep(x) L(x) with p(x) some unknown data distribution and L(·) a loss function. However, since PGD generates attack samples independently for each data sample based on L(·), the procedure does not necessarily lead to good generalization in terms of risk optimization. In this paper, we achieve the goal by proposing distributionally adversarial attack (DAA), a framework to solve an optimal adversarial-data distribution, a perturbed distribution that satisfies the L∞ constraint but deviates from the original data distribution to increase the generalization risk maximally. Algorithmically, DAA performs optimization on the space of potential data distributions, which introduces direct dependency between all data points when generating adversarial samples. DAA is evaluated by attacking state-of-the-art defense models, including the adversarially-trained models provided by MIT MadryLab. Notably, DAA ranks the first place on MadryLab’s white-box leaderboards, reducing the accuracy of their secret MNIST model to 88.56% (with l∞ perturbations of ε = 0.3) and the accuracy of their secret CIFAR model to 44.71% (with l∞ perturbations of ε = 8.0). Code for the experiments is released on https://github.com/tianzheng4/Distributionally-Adversarial-Attack.

Torque Modelling and Experiment in Pelleting Process of Rotated Roll Forming

2012 ◽  
Vol 184-185 ◽  
pp. 609-613
Author(s):  
Kai Wu ◽  
Yu Sun ◽  
Bin Bin Peng
Keyword(s):  
Pressure Distribution ◽  
Powder Material ◽  
Structural Design ◽  
Experimental Results ◽  
Distribution Model ◽  
Roll Forming ◽  
Testing System ◽  
Force Model ◽  
Optimal Structural Design

First the extruding force model of isotropic powder material passing through the die hole in pelleting process was founded, then the pressure distribution model in the extruding areas was built. Based on the two models, the torque model in pelleting process of rotated roll forming was developed. The experiments were carried out on the special designed pellet mill and the wireless torque testing system was used to analysis the torque datum. It is shown the computing datum is very close to the experimental results. The researches are helpful to the optimal structural design, energy consume reduction and proper use of the pellet mill in practice.

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