多进制小波的遥感影像融合对比分析

提出了利用多进制小波进行融合的方案,目标是为了深度挖掘小波在遥感影像像素级融合方面的应用。采用多进制小波进行融合的优势是在减少能量损失的前提下使高频分量的分解方向更加细化,重构时频段组合更加灵活。首先从理论方面阐述了多进制小波和二进制小波(包)的异同,通过对比分析在像素级遥感影像融合实验结果,同时结合目视效果和多种评价指标,发现对于中分辨率——北京一号遥感影像,用多进制（M=4时）db5小波对全色和多光谱遥感影像进行融合的效果不亚于采用二进制小波(包)的融合效果。实验结果表明,小波融合的效果取决于小波基的选取、分解级数,也因处理对象而不同,但在采用相同的小波基的基础上,多进制小波比二进制小波(包)融合效果在目视效果和融合指标两方面都略胜一筹。其为小卫星数据在中国水资源、灾害、考古等领域的重点应用推广的数据处理方面打下基础,同时可为其他不同分辨率遥感影像的类似处理起到示范作用。     

利用CPU时间戳实现局域网时间同步的新机制

当前在网络应用中,经常需要多台计算机协同工作,因而要有一个统一时钟。提出一种利用CPU内部高精度时间戳实现局域网时钟同步的新机制,并用VC++语言编写了服务端与客户端软件,且在Windows XP 和Windows 2000环境的小型局域网进行测试。结果表明,该算法同步精度较高,且系统运行稳定。     

钢铁企业全厂仪表维修设施设计初探

以我国某大型钢铁企业全厂仪表维修设施的设计方案为例,简要说明了仪修设施的设计原则、全厂仪表维修的一般解决方案,较详细地介绍了仪修设施专业划分、实验室及维修车间的设置和主要设备构成,为该类项目的设计和工程建设提供一定参考。     

基于全电子设计方案的区域计算机联锁系统

介绍了基于全电子设计方案的区域性计算机联锁控制系统的技术方案和系统构成。详细阐述了系统中主控站和被控站设备的组成、功能和特点,给出了系统详细的结构框图。系统对于降低工程造价、充分发挥计算机性能、提高运输组织效率具有重要作用。     

Using visual and text features for direct marketing on multimedia messaging services domain

Traditionally, direct marketing companies have relied on pre-testing to select the best offers to send to their audience. Companies systematically dispatch the offers under consideration to a limited sample of potential buyers, rank them with respect to their performance and, based on this ranking, decide which offers to send to the wider population. Though this pre-testing process is simple and widely used, recently the industry has been under increased pressure to further optimize learning, in particular when facing severe time and learning space constraints. The main contribution of the present work is to demonstrate that direct marketing firms can exploit the information on visual content to optimize the learning phase. This paper proposes a two-phase learning strategy based on a cascade of regression methods that takes advantage of the visual and text features to improve and accelerate the learning process. Experiments in the domain of a commercial Multimedia Messaging Service (MMS) show the effectiveness of the proposed methods and a significant improvement over traditional learning techniques. The proposed approach can be used in any multimedia direct marketing domain in which offers comprise both a visual and text component.

Modeling finite buffer effects on TCP traffic over an IEEE 802.11 infrastructure WLAN

The network scenario is that of an infrastructure IEEE 802.11 WLAN with a single AP with which several stations (STAs) are associated. The AP has a finite size buffer for storing packets. In this scenario, we consider TCP-controlled upload and download file transfers between the STAs and a server on the wireline LAN (e.g., 100 Mbps Ethernet) to which the AP is connected. In such a situation, it is well known that because of packet losses due to finite buffers at the AP, upload file transfers obtain larger throughputs than download transfers. We provide an analytical model for estimating the upload and download throughputs as a function of the buffer size at the AP. We provide models for the undelayed and delayed ACK cases for a TCP that performs loss recovery only by timeout, and also for TCP Reno. The models are validated in comparison with NS2 simulations.     

A neuro-genetic based short-term forecasting framework for network intrusion prediction system

Information systems are one of the most rapidly changing and vulnerable systems, where security is a major issue. The number of security-breaking attempts originating inside organizations is increasing steadily. Attacks made in this way, usually done by ＂authorized＂ users of the system, cannot be immediately traced. Because the idea of filtering the traffic at the entrance door, by using firewalls and the like, is not completely successful, the use of intrusion detection systems should be considered to increase the defense capacity of an information system. An intrusion detection system （IDS） is usually working in a dynamically changing environment, which forces continuous tuning of the intrusion detection model, in order to maintain sufficient performance. The manual tuning process required by current IDS depends on the system operators in working out the tuning solution and in integrating it into the detection model. Furthermore, an extensive effort is required to tackle the newly evolving attacks and a deep study is necessary to categorize it into the respective classes. To reduce this dependence, an automatically evolving anomaly IDS using neuro-genetic algorithm is presented. The proposed system automatically tunes the detection model on the fly according to the feedback provided by the system operator when false predictions are encountered. The system has been evaluated using the Knowledge Discovery in Databases Conference （KDD 2009） intrusion detection dataset. Genetic paradigm is employed to choose the predominant features, which reveal the occurrence of intrusions. The neuro-genetic IDS （NGIDS） involves calculation of weightage value for each of the categorical attributes so that data of uniform representation can be processed by the neuro-genetic algorithm. In this system unauthorized invasion of a user are identified and newer types of attacks are sensed and classified respectively by the neuro-genetic algorithm. The experimental results obtained in this work show that the system achieves improvement in terms of misclassification cost when compared with conventional IDS. The results of the experiments show that this system can be deployed based on a real network or database environment for effective prediction of both normal attacks and new attacks.     

Framework of knowledge management in education based on knowledge grid

Knowledge grid is a kind of knowledge sharing environment based on the World Wide Web, and its essence is the synergy and sharing of internet knowledge resource and knowledge service. KM （knowledge management） in education based on KG （knowledge grid） makes use of the grid technology and integrates the methods and tools of KM in Education to support the whole cycle of knowledge application. Based on the advantages of KM in Education, in this paper, we construct a service-oriented model of KM in Education in order to achieve more effective knowledge sharing and knowledge service in education.     

A combinatorial intra-enterprise exchange for logistics services

The exchange of cargo capacities is a well established approach in logistics. However, only few logistics marketplaces are able to take into consideration synergies that can be generated by combining different transportation routes of different logistics carrierps. In order to exploit these synergies, we designed and implemented the combinatorial exchange mechanism ComEx for the intra-enterprise exchange of delivery orders in a logistics company organized in a profit center structure. Each profit center is able to release delivery orders to an adjacent profit center if the geographic locations of the customers allow for a reduced-cost delivery by the adjacent profit center. We demonstrate that by using the ComEx mechanism, the total cost of transportation of our logistics company can be reduced by up to 14%. Since our iterative auction mechanism is very complex and therefore resource-intensive, we reduce the complexity by applying a convex hull approach combined with a distance-based cost estimator.

人工智能实验教学探讨

本文一方面简单介绍人工智能的基本理论及知识体系结构,另一方面则结合笔者的理论实践与实验教学经验,总结出目前在整个教学过程所存在的一些问题,目的在于不断总结经验,力争能更好地完成人工智能的整个教学工作,从而提高人才培养的质量,同时也可以为其他高校提供参考。