Fast hybrid network reconfiguration for large-scale lossless interconnection networks

2016 ◽  
Author(s):  
Evangelos Tasoulas ◽  
Ernst Gunnar Gran ◽  
Tor Skeie ◽  
Bjorn Dag Johnsen
Keyword(s):  
Interconnection Networks ◽  
Large Scale ◽  
Hybrid Network ◽  
Network Reconfiguration

scholarly journals Symbiotic Organism Search Algorithm for Power Loss Minimization in Radial Distribution Systems by Network Reconfiguration and Distributed Generation Placement

2020 ◽  
Vol 2020 ◽  
pp. 1-22
Author(s):  
Tung Tran The ◽  
Sy Nguyen Quoc ◽  
Dieu Vo Ngoc
Keyword(s):  
Distributed Generation ◽  
Radial Distribution ◽  
Power Loss ◽  
Distribution Systems ◽  
Large Scale ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Distribution Networks ◽  
Network Reconfiguration ◽  
Symbiotic Organism Search

This paper proposes the Symbiotic Organism Search (SOS) algorithm to find the optimal network configuration and the placement of distributed generation (DG) units that minimize the real power loss in radial distribution networks. The proposed algorithm simulates symbiotic relationships such as mutualism, commensalism, and parasitism for solving the optimization problems. In the optimization process, the reconfiguration problem produces a large number of infeasible network configurations. To reduce these infeasible individuals and ensure the radial topology of the network, the graph theory was applied during the power flow. The implementation of the proposed SOS algorithm was carried out on 33-bus, 69-bus, 84-bus, and 119-bus distribution networks considering seven different scenarios. Simulation results and performance comparison with other optimization methods showed that the SOS-based approach was very effective in solving the network reconfiguration and DG placement problems, especially for complex and large-scale distribution networks.

scholarly journals A Hybrid Network for Large-Scale Action Recognition from RGB and Depth Modalities

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3305 ◽  
Author(s):  
Huogen Wang ◽  
Zhanjie Song ◽  
Wanqing Li ◽  
Pichao Wang
Keyword(s):  
Neural Network ◽  
Action Recognition ◽  
Canonical Correlation ◽  
Large Scale ◽  
State Of The Art ◽  
Hybrid Network ◽  
Support Vector ◽  
Multiple Modalities ◽  
Large Margin ◽  
Percentage Points

The paper presents a novel hybrid network for large-scale action recognition from multiple modalities. The network is built upon the proposed weighted dynamic images. It effectively leverages the strengths of the emerging Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) based approaches to specifically address the challenges that occur in large-scale action recognition and are not fully dealt with by the state-of-the-art methods. Specifically, the proposed hybrid network consists of a CNN based component and an RNN based component. Features extracted by the two components are fused through canonical correlation analysis and then fed to a linear Support Vector Machine (SVM) for classification. The proposed network achieved state-of-the-art results on the ChaLearn LAP IsoGD, NTU RGB+D and Multi-modal & Multi-view & Interactive ( M 2 I ) datasets and outperformed existing methods by a large margin (over 10 percentage points in some cases).

K-PROCESSOR RELIABILITY OF LARGE-SCALE RING-BASED HIERARCHICAL INTERCONNECTIONS

2002 ◽  
Vol 09 (01) ◽  
pp. 61-77
Author(s):  
M. AL-ROUSAN ◽  
O. AL-JARRAH ◽  
M. MOWAFI
Keyword(s):  
Interconnection Networks ◽  
Large Scale ◽  
Network Reliability ◽  
Multiprocessor Systems ◽  
Hierarchical Systems ◽  
Hierarchical Networks ◽  
Coherent Interface ◽  
Large Acceptance ◽  
Ring Architecture ◽  
Scalable Coherent Interface

Recently, connecting thousands of processors via interconnection networks based on multiple (hierarchical) rings has an increased interest. This is due to the large acceptance and success of the Scalable Coherent Interface (SCI) technology. The inherently weak behavior of ring architecture has led interconnection designers to consider various choices to improve the overall network reliability. An interesting choice is to use braided rings instead of the single (basic) rings in the hierarchy. In this paper, we present new formulas for computing K-processor reliability of SCI ring-based hierarchical networks in the context of large-scale multiprocessor systems. The derived formulas are general and applicable to any given systems size consisting of an arbitrary number of levels. The reliability of hierarchical systems based on the basic and braided rings is evaluated and analyzed using the derived formulas. The results show that hierarchical systems based on braided rings significantly improve the reliability of hierarchies constructed of basic rings. The results are general and not limited to systems of SCI rings; the analysis is valid for any type of rings architecture such as token and slotted rings.

Stress-Related Noradrenergic Activity Prompts Large-Scale Neural Network Reconfiguration

Science ◽  
2011 ◽  
Vol 334 (6059) ◽  
pp. 1151-1153 ◽  
Author(s):  
E. J. Hermans ◽  
H. J. F. van Marle ◽  
L. Ossewaarde ◽  
M. J. A. G. Henckens ◽  
S. Qin ◽  
...  
Keyword(s):  
Neural Network ◽  
Large Scale ◽  
Network Reconfiguration

Optimal Network Reconfiguration of Large-Scale Distribution System Using Harmony Search Algorithm

2011 ◽  
Vol 26 (3) ◽  
pp. 1080-1088 ◽  
Author(s):  
Rayapudi Srinivasa Rao ◽  
Sadhu Venkata Lakshmi Narasimham ◽  
Manyala Ramalinga Raju ◽  
A. Srinivasa Rao
Keyword(s):  
Distribution System ◽  
Large Scale ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Network Reconfiguration ◽  
Optimal Network

scholarly journals An Enhancement of Grami Based on Threshold Policy for Pattern Big Graphs

2020 ◽  
Vol 9 (3) ◽  
pp. 2943-2949
Keyword(s):  
Biological Networks ◽  
Interconnection Networks ◽  
Large Scale ◽  
Pattern Mining ◽  
Constraint Satisfaction Problem ◽  
Interaction Network ◽  
Frequent Subgraph Mining ◽  
Threshold Policy ◽  
Subgraph Mining ◽  
Frequent Subgraph

Pattern Mining is the key mechanism to manage large scale data element. Frequent subgraph mining (FSM) considers isomorphism which is a subprocess of pattern mining is a well-studied problem in the data mining. Graphs are considered as a standard structure in many domains such as protein-protein interaction network in biological networks, wired or wireless interconnection networks, web data, etc. FSM is the task of finding all frequent subgraphs from a given database i.e. a single big graph or database of many graphs, whose support is greater than the given threshold value. Many databases consider small graphs for solving complex problems. The classification of graph depends upon the application requirement. A good mining architecture may prevent a lot of memory and time. This paper follows the Grami structure for the analysis of frequent subgraph mining and also introduces the 20% threshold policy for the enhancement of the directed pattern graphs. The constraint satisfaction problem (CSP) has been discussed and analyzed using the Grami approach. The proposed model is compared to Grami on twitter dataset based on the evaluation of time and memory consumed. The proposed algorithm shows an improvement of 3-4 % for both the parameters. The results show that the performance of Grami approach has been improved which shows a 6.6% reduction in time and 21% improvement in memory consumption using the proposed approach.

Brain Network Reconfiguration During Motor Imagery Revealed by a Large-Scale Network Analysis of Scalp EEG

2018 ◽  
Vol 32 (2) ◽  
pp. 304-314 ◽  
Author(s):  
Fali Li ◽  
Chanlin Yi ◽  
Limeng Song ◽  
Yuanling Jiang ◽  
Wenjing Peng ◽  
...  
Keyword(s):  
Network Analysis ◽  
Motor Imagery ◽  
Large Scale ◽  
Brain Network ◽  
Network Reconfiguration ◽  
Scalp Eeg ◽  
Large Scale Network ◽  
Scale Network
Sign in / Sign up

Export Citation Format

Share Document