Research on Network Partitioning Based on Expanded Grey Cluster Theory

2013 ◽  
Vol 791-793 ◽  
pp. 1690-1694
Author(s):  
Ya Ning Shao ◽  
Bo Wang ◽  
Di Chen Liu
Keyword(s):  
Parallel Computing ◽  
Real Time ◽  
Voltage Stability ◽  
Short Circuit ◽  
Computation Time ◽  
Cluster Method ◽  
Network Partitioning ◽  
Cluster Theory ◽  
The Real ◽  
Identification Criterion

the voltage stability discriminant method of short circuit capacity indicators based on measured trajectory needs a great deal of computation time while dynamic Thevenin equivalent need to be done to all the nodes, it is unable to meet the requirements of rapid real-time sentenced to stability. On this foundation, this paper proposes a quick network partitioning method. Firstly, start the real-time disturbance identification criterion and pick up the bus nods triggered by the disturbance. Then split the bus nods into k subsets using Expanded Grey Cluster method. Finally evaluate stability by parallel computing. The method is proved simple, efficient and practical through simulation.

scholarly journals A new algorithm for considering green communication and excellent sensing performance in cognitive radio networks

2020 ◽  
Vol 16 (6) ◽  
pp. 155014772093313 ◽  
Author(s):  
Tangsen Huang ◽  
Xiangdong Yin ◽  
Qingjiao Cao
Keyword(s):  
Real Time ◽  
Spectrum Sensing ◽  
Computation Time ◽  
Time Spectrum ◽  
Fusion Center ◽  
Local Data ◽  
Green Communication ◽  
Processing Efficiency ◽  
The Real ◽  
Sensing Performance

Multi-node cooperative sensing can effectively improve the performance of spectrum sensing. Multi-node cooperation will generate a large number of local data, and each node will send its own sensing data to the fusion center. The fusion center will fuse the local sensing results and make a global decision. Therefore, the more nodes, the more data, when the number of nodes is large, the global decision will be delayed. In order to achieve the real-time spectrum sensing, the fusion center needs to quickly fuse the data of each node. In this article, a fast algorithm of big data fusion is proposed to improve the real-time performance of the global decision. The algorithm improves the computing speed by reducing repeated computation. The reinforcement learning mechanism is used to mark the processed data. When the same environment parameter appears, the fusion center can directly call the nodes under the parameter environment, without having to conduct the sensing operation again. This greatly reduces the amount of data processed and improves the data processing efficiency of the fusion center. Experimental results show that the algorithm in this article can reduce the computation time while improving the sensing performance.

A Classifier of Shoulder Movements for a Wearable EMG-Based Device

2017 ◽  
Vol 02 (02) ◽  
pp. 1740003
Author(s):  
Giuseppina Gini ◽  
Lisa Mazzon ◽  
Simone Pontiggia ◽  
Paolo Belluco
Keyword(s):  
Real Time ◽  
Computation Time ◽  
Body Part ◽  
Time Constraints ◽  
Pattern Recognition Method ◽  
Noninvasive Method ◽  
Recognition Method ◽  
Two Phase ◽  
The Real ◽  
User Intentions

Prostheses and exoskeletons need a control system able to rapidly understand user intentions; a noninvasive method is to deploy a myoelectric system, and a pattern recognition method to classify the intended movement to input to the controller. Here we focus on the classification phase. Our first aim is to recognize nine movements of the shoulder, a body part seldom considered in the literature and difficult to treat since the muscles involved are deep. We show that our novel sEMG two-phase classifier, working on a signal window of 500[Formula: see text]ms with 62[Formula: see text]ms increment, has a 97.7% accuracy for nine movements and about 100% accuracy on five movements. After developing the classifier using professionally collected sEMG data from eight channels, our second aim is to implement the classifier on a wearable device, composed by the Intel Edison board and a three-channel experimental portable acquisition board. Our final aim is to develop a complete classifier for dynamic situations, considering the transitions between movements and the real-time constraints. The performance of the classifier, using three channels, is about 96.9%, the classification frequency is 62[Formula: see text]Hz, and the computation time is 16[Formula: see text]ms, far less than the real-time constraint of 300[Formula: see text]ms.

Signal Collection and Analysis on CO2 Arc Welding Based on DSP

2010 ◽  
Vol 29-32 ◽  
pp. 997-1002
Author(s):  
Wen Jin Dai ◽  
Xiang Jie Chen
Keyword(s):  
Real Time ◽  
Arc Welding ◽  
Short Circuit ◽  
Welding Time ◽  
The Real ◽  
Signal Collection ◽  
The Way

On the base for TMS320F2407DSDSP, it can collect the electricity signal from the CO2 arc welding, and pick–up the character parameters for the frequency distributing in the short circuit time and arc welding time. It realizes the real-time inspection by the way for analyzing the dynamic current and voltage, U-I graphic and the character parameter during the welding.

On-line Assessment of Voltage Stability using Synchrophasor Technology

2017 ◽  
Vol 8 (1) ◽  
pp. 1 ◽  
Author(s):  
Satyendra Pratap Singh ◽  
S.P. Singh
Keyword(s):  
Real Time ◽  
Voltage Stability ◽  
Measurement Unit ◽  
The Real ◽  
Minimum Number ◽  
On Line ◽  
Successive Change ◽  
Observable Data ◽  
Bus Voltage

Series of blackouts encountered in recent years in power system have been occurred because either of voltage or angle instability or both together was not detected within time and progressive voltage or angle instability further degraded the system condition, because of increase in loading. This paper presents the real-time assessment methodology of voltage stability using Phasor Measurement Unit (PMU) with observability of load buses only in power network. PMUs are placed at strategically obtained location such that minimum number of PMU’s can make all load buses observable. Data obtained by PMU’s are used for voltage stability assessment with the help of successive change in the angle of bus voltage with respect to incremental load, which is used as on-line voltage stability predictor (VSP). The real-time voltage phasors obtained by PMU’s are used as real time voltage stability indicator. The case study has been carried out on IEEE-14 bus system and IEEE-30 bus systems to demonstrate the results.

Handling Overruns and Underruns of Real-Time Processes With Precedence and Exclusion Relations Using a Pre-Run-Time Schedule

2013 ◽  
Author(s):  
Jia Xu
Keyword(s):  
Real Time ◽  
Computation Time ◽  
Total System ◽  
Time Process ◽  
Time Schedule ◽  
Worst Case ◽  
Processor Time ◽  
The Real ◽  
Actual Computation ◽  
Run Time

Many embedded systems applications have hard timing requirements where real-time processes with precedence and exclusion relations must be completed before specified deadlines. This requires that the worst-case computation times of the real-time processes be estimated with sufficient precision during system design, which sometimes can be difficult in practice. If the actual computation time of a real-time process during run-time exceeds the estimated worst-case computation time, an overrun will occur, which may cause the real-time process to not only miss its own deadline, but also cause a cascade of other real-time processes to also miss their deadline, possibly resulting in total system failure. However, if the actual computation time of a real-time process during run-time is less than the estimated worst-case computation time, an underrun will occur, which may result in under-utilization of system resources. This paper describes a method for handling underruns and overruns when scheduling a set of real-time processes with precedence and exclusion relations using a pre-run-time schedule. The technique effectively tracks and utilizes unused processor time resources to reduce the chances of missing real-time process deadlines, thereby providing the capability to significantly increase both system utilization and system robustness in the presence of inaccurate estimates of the worst-case computation times of real-time processes.

Parallel Route Optimization Algorithm of Central Guidance

2013 ◽  
Vol 380-384 ◽  
pp. 1571-1575
Author(s):  
Hong Chen ◽  
Hu Xing Zhou ◽  
Juan Meng
Keyword(s):  
Parallel Computing ◽  
Real Time ◽  
Data Storage ◽  
Route Optimization ◽  
Guidance System ◽  
Network Data ◽  
Traffic Network ◽  
Computing Technique ◽  
The Real ◽  
Guangzhou City

To solve the problem that the central guidance system takes too long time to calculate the shortest routes between all node pairs of network which can not meet the real-time demand of central guidance, this paper presents a central guidance parallel route optimization method based on parallel computing technique involving both route optimization time and travelers preferences by means of researching three parts: network data storage based on an array, multi-level network decomposition with travelers preferences considered and parallel shortest route computing of deque based on messages transfer. And based on the actual traffic network data of Guangzhou city, the suggested method is verified on three parallel computing platforms including ordinary PC cluster, Lenovo server cluster and HP workstations cluster. The results show that above three clusters finish the optimization of 21.4 million routes between 5631 nodes of Guangzhou city traffic network in 215, 189 and 177 seconds with the presented method respectively, which can completely meet the real-time demand of the central guidance.

3D Medical Images Registration Based on GPU Parallel Computing

2012 ◽  
Vol 241-244 ◽  
pp. 3010-3013 ◽  
Author(s):  
Lu Meng
Keyword(s):  
Parallel Computing ◽  
Image Registration ◽  
Real Time ◽  
Clinical Application ◽  
Medical Image ◽  
Three Dimensional ◽  
Medical Image Registration ◽  
Time Requirement ◽  
The Real ◽  
Gpu Parallel Computing

Real time 3D medical image registration method is key technology of medical image processing, especially in surgical operation navigation. However, current 3D medical image registration methods are time-consuming, which can’t meet the real time requirement of clinical application. To solve this problem, this paper presented a high performance computational method based on CUDA ( Compute Unified Device Architecture), which took full advantage of GPU parallel computing under CUDA architecture combined with image multiple scale and maximum mutual information to make fast registration of three dimensional medical image. Experiments showed that this algorithm can greatly accelerate the computational speed of registration of three dimensional medical image, and meet the real time requirement of clinical application.

scholarly journals A New Method of Command and Guide the Unbalanced Distribution of Target

2018 ◽  
Vol 214 ◽  
pp. 03001
Author(s):  
Yangyang Gao ◽  
Minjian Yu ◽  
Hanghang You ◽  
Huiming Ji
Keyword(s):  
Real Time ◽  
Computation Time ◽  
New Method ◽  
Allocation Scheme ◽  
Allocation Model ◽  
The Real ◽  
Hungarian Algorithm ◽  
Air Combat ◽  
Simulation Results ◽  
Target Allocation

In the process of air combat implementation, the real-time command and guidance of air pilot is the key element of air combat irreplaceable. In this paper, a multi-aircraft air combat command and guidance target allocation model is established based on the actual air combat situation. The unbalanced target allocation problem of multi-aircraft air combat command and guidance is studied by combining Hungarian algorithm and evolutionary Hungarian algorithm. Finally, two cases of increasing aircraft number and decreasing aircraft number are simulated and studied. Simulation results show that the method can shorten the computation time and ensure the overall command and guidance advantages of the target allocation scheme effectively.

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