Li P.,Wenzhou University |
Chen Y.R.,Wenzhou University |
Yang Z.Y.,Chongqing City Management Vocational College
Applied Mechanics and Materials | Year: 2014
For the problem of modeling high -mix and variable-batch flow manufacturing system, the thesis focus on method of mixed processing task and mechanism of constructing manufacture unit. First, decompose the processing task into Manufacturing Task Tree (MTT for short) according to the level of ‘orders-products-components-fittings-parts’. Then decompose each level node of the MTT to task cell. Then on the basis of processing task, we construct the framework of processing tasks management for group mixed flow manufacturing and analyze the mechanisms of building manufacturing unit for high-mix and variable-batch flow manufacturing system. Finally, we established the model of high-mix and variable-batch flow manufacturing system. © (2014) Trans Tech Publications, Switzerland.
Chen S.,Southwest University |
Peng M.,Chongqing City Management Vocational College |
Xiong H.,Southwest University |
Yu X.,Southwest University
Journal of Electrical and Computer Engineering | Year: 2016
Intrusion detection needs to deal with a large amount of data; particularly, the technology of network intrusion detection has to detect all of network data. Massive data processing is the bottleneck of network software and hardware equipment in intrusion detection. If we can reduce the data dimension in the stage of data sampling and directly obtain the feature information of network data, efficiency of detection can be improved greatly. In the paper, we present a SVM intrusion detection model based on compressive sampling. We use compressed sampling method in the compressed sensing theory to implement feature compression for network data flow so that we can gain refined sparse representation. After that SVM is used to classify the compression results. This method can realize detection of network anomaly behavior quickly without reducing the classification accuracy. © 2016 Shanxiong Chen et al.
Peng M.L.,Chongqing City Management Vocational College |
Huang A.M.,Chongqing City Management Vocational College
Applied Mechanics and Materials | Year: 2011
Many network application technology need the algorithm for multi-dimensional packet classification, for example,network security,load balancing,router policy, QoS etc. Considering the levels of multiattribute packet classified are excessive and traverse rule table times without number for matching classification rule, so efficiency is lower. A packet classification algorithm based on decision tree is put forward in the paper. As compared with some traditional packet classification matching algorithms, because three data are adopted including information gain, information gain ratio and Gini to solve attribute selection measurement, accuracy and matching efficiency are both advanced obviously. © Trans Tech Publications.
Wang X.,Chongqing City Management Vocational College |
Wang W.,Chongqing City Management Vocational College |
Yao J.,Chongqing City Management Vocational College
Energy Education Science and Technology Part A: Energy Science and Research | Year: 2014
In order to improve the quality of service (QoS) in ultra wide-band (UWB) wireless sensor networks with multi-class traffic, a distributed transmission scheduling scheme is proposed, which does not need any centralized controller. The proposed scheme dynamically schedules different types of traffic to minimize the waiting time for the traffic with lower priority, while promising QoS of traffic with higher priority. Furthermore, an energy-efficient transmission rate adaptation scheme is introduced to save transmission energy consumption. Under this scheme, the transmitter adaptively adjusts UWB physical-layer parameters to avoid useless energy consumption. Simulation results show that the average waiting time experienced by low-priority traffic packets can cut down at most nearly 80% while the QoS of the high-priority traffic is guaranteed , and the average consumed energy per packet can be saved more than 50%. © Sila Science. All Rights Reserved.