JOPENS系统汇集REFTEK实时数据的实践应用

以下关虚拟地震台网为例,介绍利用JOPENS系统汇集REFTEK系统实时数据流的实验及数据汇集的相关步骤,通过宾川人工爆破实验,检验数据汇集系统在地震定位方面的可靠性。

A new data acquisition and processing system for profiling sonar

A multi-beam chirp sonar based on IP connections and DSP processing nodes was proposed and designed to provide an expandable system with high-speed processing and mass-storage of real-time signals for multi-beam profiling sonar.The system was designed for seabed petroleum pipeline detection and orientation,and can receive echo signals and process the data in real time,refreshing the display 10 times per second.Every node of the chirp sonar connects with data processing nodes through TCP/IP. Merely by adding nodes,the system’s processing ability can be increased proportionately without changing the software.System debugging and experimental testing proved the system to be practical and stable.This design provides a new method for high speed active sonar.

A POWER SYSTEM INTELLIGENT TRAINING SIMULATOR BASED ON TRANSPUTER

The simulator is a combination of computational functions describing physical system, distributed rule and knowledge bases, and a reasoning machine, all executing in parallel. The hardware,comprising two levels of multiprocessor network-- a T800 transputer and STD--BUS computer systems--and analogue and I/O boards, has been used successfully to train and simulate a 100 MW power system at a low cost. The system has wide applications in industry.

Fast distributed and parallel pre-processing on massive satellite data using grid computing

Distributed/parallel-processing system like sun grid engine(SGE) that utilizes multiple nodes/cores is proposed for the faster processing of large sized satellite image data. After verification, distributed process environment for pre-processing performance can be improved by up to 560.65% from single processing system. Through this, analysis performance in various fields can be improved, and moreover, near-real time service can be achieved in near future.

Parallel scheduling strategy of web-based spatial computing tasks in multi-core environment

In order to improve the concurrent access performance of the web-based spatial computing system in cluster,a parallel scheduling strategy based on the multi-core environment is proposed,which includes two levels of parallel processing mechanisms.One is that it can evenly allocate tasks to each server node in the cluster and the other is that it can implement the load balancing inside a server node.Based on the strategy,a new web-based spatial computing model is designed in this paper,in which,a task response ratio calculation method,a request queue buffer mechanism and a thread scheduling strategy are focused on.Experimental results show that the new model can fully use the multi-core computing advantage of each server node in the concurrent access environment and improve the average hits per second,average I/O Hits,CPU utilization and throughput.Using speed-up ratio to analyze the traditional model and the new one,the result shows that the new model has the best performance.The performance of the multi-core server nodes in the cluster is optimized;the resource utilization and the parallel processing capabilities are enhanced.The more CPU cores you have,the higher parallel processing capabilities will be obtained.

Dynamic Distribution Model with Prime Granularity for Parallel Computing

Dynamic distribution model is one of the best schemes for parallel volume rendering. How- ever, in homogeneous cluster system.since the granularity is traditionally identical, all processors communicate almost simultaneously and computation load may lose balance. Due to problems above, a dynamic distribution model with prime granularity for parallel computing is presented. Granularities of each processor are relatively prime, and related theories are introduced. A high parallel performance can be achieved by minimizing network competition and using a load balancing strategy that ensures all processors finish almost simultaneously. Based on Master-Slave-Gleaner ( MSG) scheme, the parallel Splatting Algorithm for volume rendering is used to test the model on IBM Cluster 1350 system. The experimental results show that the model can bring a considerable improvement in performance, including computation efficiency, total execution time, speed, and load balancing.

Temperature and Humidity Control System Identification Based on Neural Network in Heating and Drying System

Artificial neural network has unique advantages for massively parallel processing, distributed storage capacity and self-learning ability. The paper mainly constructs neural network identifier and neural network controller for system identification and control on temperature and hmnidity of heating and drying system of materials. And the paper introduces the structure and principles of neural network, and focuses on analyzing learning algorithm, training algorithm and limitation of the most widely applied multi-layer feed-forward neural network （ BP network） , based on which the paper proposes introducing momentum to improve BP network.

The Edge-pancyclicity of Generalized Hypercubes

Generalized hypercubes （denoted by Q（d1,d2,... ,dn）） is an important network topology for parallel processing computer systems. Some methods of forming big cycle from small cycles and links have been developed. Basing on which, we has proved that in generalized hypercubes, every edge can be contained on a cycle of every length from 3 to IV（G）I inclusive and all kinds of length cycles have been constructed. The edgepanciclieity and node-pancilicity of generalized hypercubes can be applied in the topology design of computer networks to improve the network performance.