206827796
Application of Support Vector Machine to Mobile Communications in Telephone Traffic Load of Monthly Busy Hour Prediction
Telephone traffic of busy hour is one of indicators of load capacity of telecommunication network, which has a significant meaning to dilate and modify the network. A good performance of predicting the monthly busy hour traffic load is cared about by the mobile operators. As a promising learning theory,support vector machine (SVM) has been studied and applied in a wide area, such as financial markets and weather forecast. In this paper, we use SVM to forecast monthly busy hour traffic load of two regions in Xinjiang. A good result has been achieved via an improved grid search method for the search of hyper-parameter of SVM.
16326025
Research on Positioning Technology of Xinjiang Mobile GIS
In this paper, the characteristics of Xinjiang mobile communication network are analyzed, the results show that for a reliable mobile communication network, BS (base station) and base station control BSC (base station controller) monitoring system, cable and fiber optic cable monitoring system and mobile communications troubleshooting and dispatching system should be built. Combined with GIS, GPS, GSM and geographic database technology, positioning technology for different targets and detection on fault point are studied, the results indicate that precise positioning of static target can accurately locate the failure telecom equipment in the electronic map and provide data source for the realization of real-time monitoring and management system for telecom equipment based on GIS & wireless sensor network technology. GSM-based wireless messaging communications and real-time fast positioning of dynamic target is the key to the emergency repair vehicles monitoring and dispatch system. The proposed fault determination and fault point location method can provide fast accurately fault devices location with detailed geography information and prompt the efficiency of fault management and improve the reliability of telecom network services. The above research results are important basis on the design and implementation of the system.
22165485
An Efficient Fuzzy Kohonen Clustering Network Algorithm
Fuzzy Kohonen clustering networks (FKCN) are well known for clustering analysis (unsupervised learning and self-organizing). This classification of FKCN algorithm is a set of iterative procedures that suffer some major problems, for example its constringency rate is not too fast for a large amount of datasets. To overcome these defects, an efficient fuzzy Kohonen network algorithm is proposed in this paper, which can significantly reduce the computation time required to partition a dataset into desired clusters. By introducing the threshold values and fuzzy convergence operators in the network learning procedure to adjust the learning rates dynamically, the network convergence rate is greatly improved and the error rates of dataset cluster are significantly decreased. Experimental results show the new algorithm is on average three times faster than the original FKCN algorithm. We also demonstrate that the quality of the improved FKCN is better than the original FKCN algorithm.
43405539
An Improved Method of Traffic Forecasting Based on Tariff-SASVR
Traffic forecasting is critical for mobile operators to grasp market trends and control network capacity. Therefore, an improved method of forecasting for mobile traffic is presented in this paper. The traffic is divided into the general trend part and seasonal part to forecast them respectively. The general trend is predicted by fitting the curve of general trend on tariff level; and the remaining seasonal part is predicted by simulated annealing-support vector regression machine (SASVR) which uses simulated annealing (SA) to select the super-parameters of SVR. The experimental results show that not only this method improves the prediction accuracy but it provides mobile operators with a visual expression of the relationship between traffic and the tariff level.
4409694
On route selection for interdomain traffic engineering
In this article we investigate a model of route selection for interdomain traffic engineering where routing to multiple destinations can be coordinated. We identify potential routing instability and inefficiency problems, and derive a set of practical guidelines to guarantee stability without global coordination. Using a realistic Internet topology, we show that route oscillations can happen even when a small number of ASes coordinate route selection for just a small number of destinations if the coordination does not follow our guidelines. Wc further extend our model so that ASes can adopt any route selection algorithms in a class of algorithms we call rational route selection algorithms; and the local ranking of routes of an AS can depend on ingress traffic patterns. We show that persistent route oscillations can happen in certain network settings even if the ASes strictly follow the constraints imposed by business considerations, and adopt any rational route selection algorithms.
44811386
Classification of Polarimetric SAR Data Based on Multidimensional Watershed Clustering
This paper proposes a polarimetric synthetic aperture radar (PolSAR) data classification method which applies multi-dimensional transform to identify density peaks and valleys for polarimetric signatures clustering. The new approach firstly introduces an improved maximum homogeneous region filter which can effectively preserve structure feature and polarimetric signatures. Then polarimetric signatures are extracted based on Freeman-Durden three-component composition. Finally, we obtain the classification results by multi-dimensional watershed clustering on the extracted polarimetric signatures. The effectiveness of this classification scheme is demonstrated using the full polarimetric L-band SAR imagery.
1344510
COPE: traffic engineering in dynamic networks
Traffic engineering plays a critical role in determining the performance and reliability of a network. A major challenge in traffic engineering is how to cope with dynamic and unpredictable changes in traffic demand. In this paper, we propose COPE, a class of traffic engineering algorithms that optimize for the expected scenarios while providing a worst-case guarantee for unexpected scenarios. Using extensive evaluations based on real topologies and traffic traces, we show that COPE can achieve efficient resource utilization and avoid network congestion in a wide variety of scenarios.
6971305
Video streaming over multi-hop wireless networks
In this paper, we considered the problem of transporting layered video over erroneous multi-hop wireless networks and proposed a distributed system. Our proposed distributed scheme is comprised of distributed control (DC), distributed buffer (DB) and distributed error control schemes. The DC scheme improves the efficiency of the network bandwidth usage and reduces the end-to-end delay of the streaming application. End-to-end delay jitter can be reduced by proper use of the DB nodes' buffer. Replacing the traditional EEC and ARQ with our distributed FEC and ARQ scheme reduces the error-protection overhead and ARQ delay and improves the wireless channel throughput. A layered video module is developed in GlomoSim 2.03 for video streaming application. We simulated our distributed scheme employing the layered video module in GlomoSim 2.03. Our simulation results confirm that QoS of streaming video over erroneous multi-hop wireless network can be improved using our proposed distributed scheme.
15500084
Understanding the Prediction of Transmembrane Proteins by Support Vector Machine using Association Rule Mining
With the efforts to understand protein structure, many computational approaches have been made recently. Among them, the support vector machine (SVM) methods have been recently applied and showed successful performance compared with other machine learning schemes. However, despite the high performance, the SVM approaches suffer from the problem of understandability since it is a black-box model. To overcome this limitation, this study attempted to combine the SVM with the association rule based classifier which can present the meaningful explanation about the prediction. To perform this task, a new association rule based classifier (PCPAR) was devised based on the existing classifier, CPAR, to handle the sequential data. PCPAR creates the patterns by merging the generated rules and then classifies the sequential data based on the pattern match. The experimental result presents the following: with sequential data, the PCPAR scheme shows better performance with respect to the accuracy and the number of generated patterns than CPAR method whether applied alone or combined with SVM. The combined scheme of SVMPCPAR generates more compact patterns than the combined scheme of SVM with decision tree, SVM DT, with similar performance. These patterns are easily understandable and biologically meaningful