市政排水施工中存在的问题研究

随着城市化进程的加快,在城市的生产生活中出现了大量的问题,其中就包括市政排水工程出现的问题。本文主要通过研究市政排水施工过程中存在的问题,力求寻找到科学合理的办法来解决市政排水施工过程中存在的问题,最终让城市居民舒适地生活在城市的土地上。

机构编制档案管理工作初探

机构编制的档案管理工作是一个机构或者部门发展的重要因素之一,但是由于档案管理工作存在查找难,信息不能及时存档以及保存率低的问题,导致相关部门和机构的发展不能顺利进行。而要改变这样的现状,只有从这些问题本身入手,不断地培养机构编制档案管理的工作人员,提升其专业管理能力,给管理工作提供人才保证,并在此基础之上,有效连接各个部门的相关工作,进一步完善档案的检查工作,才能让机构编制档案管理工作高效地服务于机构和相关部门的发展。

Research on internet traffic classification techniques using supervised machine learning

Interact traffic classification is vital to the areas of network operation and management. Traditional classification methods such as port mapping and payload analysis are becoming increasingly difficult as newly emerged applications （e. g. Peer-to-Peer） using dynamic port numbers, masquerading techniques and encryption to avoid detection. This paper presents a machine learning （ML） based traffic classifica- tion scheme, which offers solutions to a variety of network activities and provides a platform of performance evaluation for the classifiers. The impact of dataset size, feature selection, number of application types and ML algorithm selection on classification performance is analyzed and demonstrated by the following experiments： （1） The genetic algorithm based feature selection can dramatically reduce the cost without diminishing classification accuracy. （2） The chosen ML algorithms can achieve high classification accuracy. Particularly, REPTree and C4.5 outperform the other ML algorithms when computational complexity and accuracy are both taken into account. （3） Larger dataset and fewer application types would result in better classification accuracy. Finally, early detection with only several initial packets is proposed for real-time network activity and it is proved to be feasible according to the preliminary results.

Managing High Volume Data for Network Attack Detection Using Real-Time Flow Filtering

In this paper, we present Real-Time Flow Filter (RTFF) -a system that adopts a middle ground between coarse-grained volume anomaly detection and deep packet inspection. RTFF was designed with the goal of scaling to high volume data feeds that are common in large Tier-1 ISP networks and providing rich, timely information on observed attacks. It is a software solution that is designed to run on off-the-shelf hardware platforms and incorporates a scalable data processing architecture along with lightweight analysis algorithms that make it suitable for deployment in large networks. RTFF also makes use of state of the art machine learning algorithms to construct attack models that can be used to detect as well as predict attacks.

Benchmark solutions for MHD solver development

A benchmark solution is of great importance in validating algorithms and codes for magnetohydrodynamic(MHD) flows.Hunt and Shercliff's solutions are usually employed as benchmarks for MHD flows in a duct with insulated walls or with thin conductive walls,in which wall effects on MHD are represented by the wall conductance ratio.With wall thickness resolved,it is stressed that the solution of Sloan and Smith's and the solution of Butler's can be used to check the error of the thin wall approximation condition used for Hunt's solutions.It is noted that Tao and Ni's solutions can be used as a benchmark for MHD flows in a duct with wall symmetrical or unsymmetrical,thick or thin.When the walls are symmetrical,Tao and Ni's solutions are reduced to Sloan and Smith's solution and Butler's solution,respectively.

HASM-AD Algorithm Based on the Sequential Least Squares

The HASM(high accuracy surface modeling) technique is based on the fundamental theory of surfaces,which has been proved to improve the interpolation accuracy in surface fitting.However,the integral iterative solution in previous studies resulted in high temporal complexity in computation and huge memory usage so that it became difficult to put the technique into application,especially for large-scale datasets.In the study,an innovative model(HASM-AD) is developed according to the sequential least squares on the basis of data adjustment theory.Sequential division is adopted in the technique,so that linear equations can be divided into groups to be processed in sequence with the temporal complexity reduced greatly in computation.The experiment indicates that the HASM-AD technique surpasses the traditional spatial interpolation methods in accuracy.Also,the cross-validation result proves the same conclusion for the spatial interpolation of soil PH property with the data sampled in Jiangxi province.Moreover,it is demonstrated in the study that the HASM-AD technique significantly reduces the computational complexity and lessens memory usage in computation.