Closed-loop Network Automation with Generic Programmable Data Plane (G-PDP) : (Invited Paper)

2021 ◽  
Author(s):  
Shaofei Tang ◽  
Hui Liang ◽  
Min Wang ◽  
Tingyu Li ◽  
Zuqing Zhu
Keyword(s):  
Closed Loop ◽  
Data Plane ◽  
Loop Network

Simulation of a Closed-Loops Assembly Line

2012 ◽  
Vol 502 ◽  
pp. 127-132 ◽  
Author(s):  
L.P. Ferreira ◽  
E. Ares ◽  
G. Peláez ◽  
A. Resano ◽  
C.J. Luis-Pérez ◽  
...  
Keyword(s):  
Assembly Line ◽  
Closed Loop ◽  
Discrete Event ◽  
Specific Class ◽  
Production Lines ◽  
Closed Loops ◽  
Event Simulation ◽  
Automobile Assembly ◽  
Loop Network ◽  
The Impact

The aim of the work presented in this paper describes the development of a decision support system based on a discrete-event simulation model of an automobile assembly line. The model focuses at a very specific class of production lines with a four closed-loop network configuration. One key characteristic in the closed-loop system is that the number of pallets inside the first three loops has been made constant. The impact of the number of pallets circulating on the first three closed-loops and of the proportion of four-door car bodies on the performance of the production line has been thoroughly investigated. This has been translated into the number of cars produced per hour, in order to improve the availability of the entire manufacturing system.

scholarly journals Simulasi Modifikasi Konfigurasi Jaringan Spindel Menjadi Loop Tertutup Untuk Meningkatkan Keandalan Sistem Distribusi Menggunakan Software ETAP

2021 ◽  
Vol 13 (1) ◽  
pp. 33-39
Author(s):  
Eko Nio Rizki
Keyword(s):  
Closed Loop ◽  
Network Reliability ◽  
Short Circuit ◽  
Circuit Breaker ◽  
Open Loop ◽  
Network Configuration ◽  
Ground Fault ◽  
Overcurrent Relay ◽  
Level 3 ◽  
Loop Network

Distribution reliability network rely on to any factor such as material quality, maintenance, operational pattern, protection device and also network configuration. In the spindle network the level of network reliability is level 3 (SPLN  52-3, 1983: 5). In order to leveling up the network reliability from level 3 to level 5 (zero down time)[2][3] we need to modify the protection system from overcurrent relay and ground fault relay to line differential relay in each distribution substation. Beside that Load Break Switch in each customer cubicle substation and in the connection substation should replaced by circuit breaker. Spindle network which operated open loop in the connection substation switch to normally close operated, so it can be called as closed loop network. This modification purpose is ther is no down time in case off ground fault or phase to phase sort circuit on the network cable. Before this kind of modification and the setting applied into real network, we make a simulation using an application called ETAP and no missmatch trip from 7 time experiment  consist of ground fault and phase to phase short circuit in 7  cable

scholarly journals Prepare for the Worst - Using Facility Location to Alleviate Product Recall Risks

2020 ◽  
Vol 10 (4) ◽  
pp. 159
Author(s):  
Liufang Yao ◽  
Andrew G. Carrothers
Keyword(s):  
Supply Chain ◽  
Closed Loop ◽  
Food Product ◽  
Major Product ◽  
Location Decisions ◽  
Product Recalls ◽  
Product Recall ◽  
Effective Response ◽  
Operational Costs ◽  
Loop Network

This paper is motivated by major food product recall events in recent years, especially how the timely and effective response using post-recall management can make a difference. We consider the rare but very influential major product recalls as disruptions to the supply chain and incorporate locating reprocessing centers for the returned products to mitigate expected operational costs. We adopt the closed loop network design framework and assume the location decisions for reprocessing center take place after the product recall events. Our scenario-based analysis shows the approach is effective in both absolute and relative measures.

Seismic Inversion via Closed-Loop Fully Convolutional Residual Network and Transfer Learning

Geophysics ◽  
2021 ◽  
pp. 1-54
Author(s):  
Lingling Wang ◽  
Delin Meng ◽  
Bangyu Wu
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Seismic Data ◽  
Closed Loop ◽  
Seismic Inversion ◽  
Open Loop ◽  
Residual Network ◽  
Velocity Inversion ◽  
Good Convergence ◽  
Loop Network

Because deep learning networks can 'learn' the complex mapping function between the labeled inputs and outputs, they have shown great potential in seismic inversion. Conventional deep learning algorithms require a large amount of labeled data for sufficient training. However, in practice, the number of well logs is limited. To address this problem, we propose a closed-loop fully convolutional residual network (FCRN) combined with transfer learning strategy for seismic inversion. This closed-loop FCRN consists of an inverse network and a forward network. The inverse network predicts the inversion target from seismic data, whereas the forward network calculates seismic data from the inversion target. The inverse network is initialized by pre-training on the Marmousi2 model and fine-tuned with the limited labeled data around the wells through transfer learning, to suit the target seismic data. The forward network is initialized by training with the limited labeled data around the wells. In this way, the closed-loop network is well initialized to ensure relatively good convergence. Then, the misfit of the limited labeled data and the error between the true and the forward seismic data are used to regularize the training of the initialized closed-loop network. The inverse network of the optimized closed-loop network is used to obtain the final inversion results. The proposed work flow can be used for velocity, density, and impedance inversion from post-stack seismic data. This paper takes velocity inversion as an example to illustrate the effectiveness of the method. The experimental results show that the closed-loop FCRN with transfer learning is superior than the open-loop FCRN with better lateral continuity and velocity details. The closed-loop FCRN can effectively predict the velocity with high accuracy on the synthetic data, has good anti-noise performance, and also can be effectively used for the field data with spatial heterogeneity.

Sign in / Sign up

Export Citation Format

Share Document