Data Mining and Neural Network Techniques in Case Based System

This paper first puts forward a case based system framework based on data mining techniques. Then the paper examines the possibility of using neural networks as a method of retrieval in such a case based system. In this system we propose data mining algorithms to discover case knowledge and other algorithms.

Free-matrix-based time-dependent discontinuous Lyapunov functional for synchronization of delayed neural networks with sampled-data control

This paper is concerned with the synchronization of delayed neural networks via sampled-data control. A new technique, namely, the free-matrix-based time-dependent discontinuous Lyapunov functional approach, is adopted in constructing the Lyapunov functional, which takes advantage of the sampling characteristic of sawtooth input delay. Based on this discontinuous Lyapunov functional, some less conservative synchronization criteria are established to ensure that the slave system is synchronous with the master system. The desired sampled-data controller can be obtained through the use of the linear matrix inequality（LMI） technique. Finally, two numerical examples are provided to demonstrate the effectiveness and the improvements of the proposed methods.

A Path-Based Approach for Data Aggregation in Grid-Based Wireless Sensor Networks

Sensor nodes in a wireless sensor network （WSN） are typically powered by batteries, thus the energy is constrained. It is our design goal to efficiently utilize the energy of each sensor node to extend its lifetime, so as to prolong the lifetime of the whole WSN. In this paper, we propose a path-based data aggregation scheme （PBDAS） for grid-based wireless sensor networks. In order to extend the lifetime of a WSN, we construct a grid infrastructure by partitioning the whole sensor field into a grid of cells. Each cell has a head responsible for aggregating its own data with the data sensed by the others in the same cell and then transmitting out. In order to efficiently and rapidly transmit the data to the base station （BS）, we link each cell head to form a chain. Each cell head on the chain takes turn becoming the chain leader responsible for transmitting data to the BS. Aggregated data moves from head to head along the chain, and finally the chain leader transmits to the BS. In PBDAS, only the cell heads need to transmit data toward the BS. Therefore, the data transmissions to the BS substantially decrease. Besides, the cell heads and chain leader are designated in turn according to the energy level so that the energy depletion of nodes is evenly distributed. Simulation results show that the proposed PBDAS extends the lifetime of sensor nodes, so as to make the lifetime of the whole network longer.

Model-based predictive controller design for a class of nonlinear networked systems with communication delays and data loss

This paper discusses the model-based predictive controller design of networked nonlinear systems with communica- tion delay and data loss. Based on the analysis of the closed-loop networked predictive control systems, the model-based networked predictive control strategy can compensate for communication delay and data loss in an active way. The designed model-based predictive controller can also guarantee the stability of the closed-loop networked system. The simulation re- suits demonstrate the feasibility and efficacy of the proposed model-based predictive controller design scheme.

Proposed Caching Scheme for Optimizing Trade-off between Freshness and Energy Consumption in Name Data Networking Based IoT

Over the last few years, the Internet of Things (IoT) has become an omnipresent term. The IoT expands the existing common concepts, anytime and anyplace to the connectivity for anything. The proliferation in IoT offers opportunities but may also bear risks. A hitherto neglected aspect is the possible increase in power consumption as smart devices in IoT applications are expected to be reachable by other devices at all times. This implies that the device is consuming electrical energy even when it is not in use for its primary function. Many researchers’ communities have started addressing storage ability like cache memory of smart devices using the concept called—Named Data Networking (NDN) to achieve better energy efficient communication model. In NDN, memory or buffer overflow is the common challenge especially when internal memory of node exceeds its limit and data with highest degree of freshness may not be accommodated and entire scenarios behaves like a traditional network. In such case, Data Caching is not performed by intermediate nodes to guarantee highest degree of freshness. On the periodical updates sent from data producers, it is exceedingly demanded that data consumers must get up to date information at cost of lease energy. Consequently, there is challenge in maintaining tradeoff between freshness energy consumption during Publisher-Subscriber interaction. In our work, we proposed the architecture to overcome cache strategy issue by Smart Caching Algorithm for improvement in memory management and data freshness. The smart caching strategy updates the data at precise interval by keeping garbage data into consideration. It is also observed from experiment that data redundancy can be easily obtained by ignoring/dropping data packets for the information which is not of interest by other participating nodes in network, ultimately leading to optimizing tradeoff between freshness and energy required.

Network Based Real Time Condition Monitoring of Rotating Machinery

This paper presents the development of a network based real time condition monitoring system of rotating machinery. The system is built up in a double net structure consisting of local net (including client and server) and intranet. The client serves as a field data collector and processor that samples the vibration signals and process parameters of a machine monitored in the net and processes the sampled data. The data collected by the client are transmitted to the server that processes the data further and provides the results of the diagnosis of each machine to any distant terminals through intranet or internet. Such a structure of the monitoring system is advantageous in safety, reliability and reasonably shares the existing net resources. In order to ensure real time transmission of the data, two procedures of data transmission, virtual channel and data pool, are developed and applied in the monitoring system. The experimental results show that the monitoring system works well and is suitable to monitor a large group of rotating machines.

NetWare中FoxBASE^+共享数据库方法

介绍网络环境中的FoxBASE+ 共享数据库方法及其实用技术 .主要阐述了对数据库文件的操作 :文件的加锁、解锁 ,共享打开 ,共享操作技术和应该互斥操作的FOX命令 .

基于大数据和无线网络的采摘机器人定位技术研究

分析了5G无线网络定位原理,提出了单基站定位技术和加权质心的5G定位技术,设计了采摘机器人定位系统硬件部分,并基于大数据和无线网络实现了采摘机器人定位系统。试验结果表明:系统具有较好的定位精度,优于单基站定位方法精度,且其定位曲线与北斗定位设备曲线拟合度非常高,稳定性较好。

TP80共享 BCM—Ⅲ的 DBASE—Ⅱ数据库

本文讨论了TP801在局域网中共享DBASEⅡ数据库的一种方法.以提高计算机资源的利用率和单板机TP801的功能,为生产过程参数的实时打印报表提供了一个实用工具。

Sybase SQL Server与管理信息系统设计

从运行方式、数据库组织内容、事务管理方式、维护数据完整机制、安全保密措施、管理工具性能等方面，概括了ＳＱＬＳｅｒｖｅｒ的优点。

Modeling Reading and Replying Activities in a BBS Social Network

This paper is devoted to analyze and model user reading and replying activities in a bulletin board system （BBS） social network. By analyzing the data set from a famous Chinese BBS social network, we show how some user activities distribute, and reveal several important features that might characterize user dynamics. We propose a method to model user activities in the BBS social network. The model could reproduce power-law and non-power-law distributions of user activities at the same time. Our results show that user reading and replying activities could be simulated through simple agent-based models. Specifically, manners of how the BBS server interacts with Internet users in the Web 2.0 application, how users organize their reading lists, and how user behavioral trait distributes are the important factors in the formation of activity patterns.

Enhanced Prospect Definition Using Well and 4D Seismic Data in a Niger Delta Field

We carried out time-lapse analysis in a producing Niger Delta X-field, by first investigating the response and sensitivity of rock properties/attributes to lithology and pore fill in 3-D cross plot domain and by Gassmann’s fluid substitution modeling. Furthermore, 4-D seismic data were inverted into acoustic impedance volumes through model based inversion scheme. This served as input into a multi-attribute neural network algorithm for the extraction of rock attribute volumes based on the results of the petrophysical log analysis. Subsequently, horizon slices of rock properties/ attributes were extracted from the inverted seismic data and analyzed. In this way, we mapped hydrocarbon depleted wells in the field, and identified probable by-passed hydrocarbon zones. Thus, the integration of well and time lapse seismic (4-D) data in reservoir studies has remarkably improved information on the reservoir economic potential, and enhanced hydrocarbon recovery factor.

基于LBSN数据聚类分析的城市POI感知方法

城市POI的分布情况客观反映了一个城市各行各业的发展情况,传统获取POI的测绘手段成本高、更新周期长、时效性差,而基于位置的社交网络(Location-Based Social Network,LBSN)平台的发展为实现城市POI的感知提供了一种新思路。本文提出一种基于LBSN数据聚类分析的城市POI感知方法,首先,对LBSN数据进行预处理,包括清洗重复数据、删除无效数据、数据预分类等,以提高数据的有效性;其次,提出一种改进的DBSCAN算法,对处理后的数据进行聚类分析,从而得到准确度较高的城市各类POI分布情况。实验结果表明,与传统的DBSCAN算法以及K-means算法相比,本文提出的算法有更好的聚类效果,且在聚类指标上有更大的CH指数值和更小的DBI指数值。

计算机网络通信中的数据加密技术研究

基于网络入侵攻击、匿名用户访问的安全防护管理需求,利用Hadoop分布式软件框架、CP-ABE(Ciphertext Policy ABE)属性基加密算法,建构起访问身份认证、密钥共享的云服务控制平台,由属性授权中心机构自主生成网络通信的公钥、主私钥,数据所有者基于CP-ABE加密算法的双线性映射规则作出数据属性集合的密钥加密,数据接收者基于访问结构的密文、解密密钥完成数据属性集合解密操作,并在公有云服务平台形成加密数据存储和细粒度共享,有效提升计算机网络通信的数据访问、传输管理安全性。

滇中引水工程中数字孪生技术的应用研究

为更好地采用信息化手段为滇中引水工程建设及运行管理提供更科学的决策,提出采用数字孪生技术构建智慧滇中引水体系的解决方案。在总结目前滇中引水工程信息化建设的基础上,对标水利工程数字孪生建设要求,搭建滇中引水工程数字孪生系统架构,从信息采集、数据传输、云计算中心和数字孪生平台几个方面全面系统地进行构建,并根据工程实际提出水源泵站、深埋隧洞和运行维护3个数字孪生运用场景。研究结果可有效提高滇中引水工程智能安全的建设管理水平和调水能力,也可供其他引水工程数字孪生建设借鉴。

Test Data Generation for Stateful Network Protocol Fuzzing Using a Rule-Based State Machine

To improve the efficiency and coverage of stateful network protocol fuzzing, this paper proposes a new method, using a rule-based state machine and a stateful rule tree to guide the generation of fuzz testing data. The method first builds a rule-based state machine model as a formal description of the states of a network protocol. This removes safety paths, to cut down the scale of the state space. Then it uses a stateful rule tree to describe the relationship between states and messages, and then remove useless items from it. According to the message sequence obtained by the analysis of paths using the stateful rule tree and the protocol specification, an abstract data model of test case generation is defined. The fuzz testing data is produced by various generation algorithms through filling data in the fields of the data model. Using the rule-based state machine and the stateful rule tree, the quantity of test data can be reduced. Experimental results indicate that our method can discover the same vulnerabilities as traditional approaches, using less test data, while optimizing test data generation and improving test efficiency.

基于命名数据网络的车联网信息转发策略

针对在车联网中由于节点高移动性导致的网络拓扑动态变化问题和传统网络潜在的广播风暴问题,提出一种基于网络拓扑预测的车联网信息转发策略。首先根据车联网中节点性能、车辆运行状态属性、运动轨迹、链路属性等,采用动态网络拓扑演化算法进行拓扑预测,构建当前网络拓扑;再使用最短路径算法构建多源信息转发表,根据信息转发表进行信息转发;最后在转发表失效的情况下,根据节点和链路属性选择最佳邻居节点转发数据,同时,进一步完善基于拓扑预测的内容转发;最后仿真结果表明,与其他转发策略相比,本文所提出的转发策略能有效减少内容获取时延,提高请求命中率。

网络思政、虚拟思政、数字思政、数据思政、智能思政与智慧思政概念及其关系辨析

网络思政、虚拟思政、数字思政、数据思政、智能思政与智慧思政是新形势下我国思想政治教育体系的重要组成部分。正确理解和把握这组概念的具体涵义,辨析其相互关系有利于思想政治教育研究领域的学术交流和知识生产,对思想政治教育方法的创新和发展具有重要意义。本文从文本释义、内涵和外延三个维度厘清这组概念的基本涵义,从“术”“学”“道”出发分析了概念之间的内在联系,辨析了这组概念在侧重点、实践依据以及具体内容之间的差异。

DAWN: A Density Adaptive Routing for Deadline-Based Data Collection in Vehicular Delay Tolerant Networks

Vehicular Delay Tolerant Networks （DTN） use moving vehicles to sample and relay sensory data for urban areas, making it a promising low-cost solution for the urban sensing and infotainment applications. However, routing in the DTN in real vehicle fleet is a great challenge due to uneven and fluctuant node density caused by vehicle mobility patterns. Moreover, the high vehicle density in urban areas makes the wireless channel capacity an impactful factor to network performance. In this paper, we propose a local capacity constrained density adaptive routing algorithm for large scale vehicular DTN in urban areas which targets to increase the packet delivery ratio within deadline, namely Density Adaptive routing With Node deadline awareness （DAWN）. DAWN enables the mobile nodes awareness of their neighbor density, to which the nodes＇ transmission manners are adapted so as to better utilize the limited capacity and increase the data delivery probability within delay constraint based only on local information. Through simulations on Manhattan Grid Mobility Model and the real GPS traces of 4960 taxi cabs for 30 days in the Beijing city, DAWN is demonstrated to outperform other classical DTN routing schemes in performance of delivery ratio and coverage within delay constraint. These simulations suggest that DAWN is practically useful for the vehicular DTN in urban areas.

通信网络基站能耗管理中大数据智能化技术的应用

随着用户数量的增加和数据流量的爆发式增长,通信网络基站的运营和管理面临着巨大的挑战。文章重点表述大数据智能化技术在通信网络基站能耗管理中的应用,通过基站能耗数据收集处理与封装、建立预测模型以及实现智能调度等精细化管理,实现节能减排,提高网络运营效率,并提升通信网络基站的稳定性和服务质量。