机车状态管理信息系统

基于PC-104模块而开发的机车状态管理信息系统，可以在机车运行过程中将各种状态信息记录下来，并在机车运行结束后对此数据进行转储，处理和分析，在此基础上，可经过积累数据而建立知识库，以采用更先进的诊断方法。     

基于数据挖掘的小行星探测轨道误差分析与校正

为及时发现小行星探测轨道误差，快速纠正，降低损失，提出基于数据挖掘的小行星探测轨道误差分析与校正方法。首先从小行星探测器运行数据中提取探测器运行特征，根据运行特征利用数据挖掘中的随机森林法实施误差挖掘，判断探测数据是否存在误差，然后根据分析误差，利用基于控制器的校正方法实施小行星探测轨道误差校正，让探测器回到预期轨道，最后测试结果表明与其它方法相比，本方法ROC曲线下的AUC值为0.9225，更接近1.0，误差分析准确度更高；校正后，探测轨道更贴合预期探测轨道，达到校正目标。     

基于物联网技术的新能源电网运行状态监测方法

提出了基于物联网技术的新能源电网运行状态监测方法。利用物联网技术选取ZigBee通信网络中的网状网络以及树形网络，来收集新能源电网运行状态数据，并通过无线网络对监测区域中的参数进行智能检测；根据监测到的运行状态数据搭建新能源电网运行状态监测模型；利用电流监测原理对运行状完成监测。在实验中，通过监测器的布置进行运行状态的监控，并对提出的方法进行验证，经实验结果分析证明，发现采用基于物联网技术的新能源电网运行状态监测方法监测到的运行异常次数的误差较小，可以将误差控制在20%以内。此方法可以有效地对新能源电网的状态运行进行监测，可以更精准地对新能源电网的运行状态进行观察和研究，提高了电网运行的安全程度。     

基于Dymola的大数据可视化教学辅助系统

大数据可视化教学辅助系统运算较为复杂，导致系统中央处理器（CentralProcessingUnit,CPU）占用率增加，影响系统的稳定运行，因此设计基于Dymola的大数据可视化教学辅助系统。硬件方面，设计MC68HC908GP32芯片和SDIN5C2-8GB存储器。软件方面：建立教学辅助权限控制模块，对不同的权限实体采用不同的权限控制；基于Dymola构建大数据可视化教学辅助模型，简化系统运算程序，从而提高系统的运行稳定性。测试结果表明，该系统的CPU占用率更低，能够保持良好的运行状态。     

医院财务预算编审信息化管理系统设计

针对系统负载过大，运行效率降低的问题，在信息化背景下，设计医院财务部门预算编审管理系统。基于B/S架构搭建系统数据输入层、数据处理层和数据输出层等三层体系架构。在当前预算编审管理系统基础数据库的基础上，通过计算活跃因子重新配置其存储结构，实现分布式存储。系统测试证明，不同负载率下，本系统运行所产生的能耗量较低，系统反应时间更短，运行效率更快，解决了当前管理系统存在的问题。     

基于连接池的数据访问层设计

在当今的Web应用中，数据访问无疑是其中不町或缺的一环，而在J2EE的分层结构设计中，数据访问层的设计对整个J2EE应用是否能良好的运行更起着至关重要的作用。本文在综合各种数据访问层的设计方案后提出了一种在J2EE环境下基于连接池的数据访问层的设计，简化了数据操作，具有良好的复用性。     

9步打造无忧备份BackupOnDemand

以往的备份操作往往需要我们手工执行，有些马大哈级的用户就常忘记备份，以致数据丢失后懊悔不已．后来出现了比较自动化的工具能让计算机按固定周期对重要数据进行自动备份，但它对两次备份之间发生的数据损失毫无办法．现在．我们有了更有效的备份工具BackupOnDemand．它可以运行于后台，自动监视文件的变化，最大限度地保证了数据的安全．     

车辆数据在智慧服务中的应用

在“软件定义汽车,数据驱动汽车”这一时代浪潮驱动下,汽车产业开始朝着智能网联方向发展。智能网联汽车在运行时会产生大量的数据,这些数据记录了车辆运行时的状态、路侧的信息以及驾驶员相关的操作痕迹等,并且描绘了车辆和车主的画像。挖掘这些数据的内容,能洞察车辆当前是否正常运行,了解车主在驾车时的需求,为车主提供更智能和更方便的服务。基于此,本文对车辆数据在智慧服务中的应用进行了阐述。     

加权LOF结合上下文判断的云环境中服务运行数据异常检测方法

云环境中服务运行数据是服务运行状态的反映，如果服务运行数据出现异常将会影响相关软件的运行和用户的使用。传统的软件异常检测方法通常忽略软件运行数据各维度属性提供的信息量及软件运行时的上下文环境，从而影响异常检测的准确率。因此，提出一种加权LOF结合上下文判断的云环境中服务运行数据异常检测方法，首先使用信息熵法给服务运行数据的各维度属性赋权，使用改进的加权LOF算法对服务运行数据进行初次异常判断；然后综合考虑服务运行时的上下文信息，对服务运行数据进行二次异常判断后得到相应结果。实验表明，此方法能够有效检测出云环境中的服务运行数据异常。     

泓格科技推出XP-8000

泓格科技推出具备内建Windows Embedded Standard 2009操作系统之全新控制器XP-8000系列。XP-8000本身具有无风扇及无硬盘之设计，更可在宽温（-25～75℃）之工作环境下稳定运行。由于具有高规格的硬件配备，因此可大量储存数据，更可快速并从容不迫、游刃有余地处理大量信息，提供给用户更多元化的选择。     

Soft computing in big data intelligent transportation systems

The academic and industry have entered big data era in many computer software and embedded system related fields. Intelligent transportation system problem is one of the important areas in the real big data application scenarios. However, it is posing significant challenge to manage the traffic lights efficiently due to the accumulated dynamic car flow data scale. In this paper, we present NeverStop, which utilizes genetic algorithms and fuzzy control methods in big data intelligent transportation systems. NeverStop is constructed with sensors to control the traffic lights at intersection automatically. It utilizes fuzzy control method and genetic algorithm to adjust the waiting time for the traffic lights, consequently the average waiting time can be significantly reduced. A prototype system has been implemented at an EBox-II terminal device, running the fuzzy control and genetic algorithms. Experimental results on the prototype system demonstrate NeverStop can efficiently facilitate researchers to reduce the average waiting time for vehicles.     

Context-based object-class recognition and retrieval by generalized correlograms

We present a novel approach for retrieval of object categories based on a novel type of image representation: the Generalized Correlogram (GC). In our image representation, the object is described as a constellation of GCs where each one encodes information about some local part and the spatial relations from this part to others (i.e., the part's context). We show how such a representation can be used with fast procedures that learn the object category with weak supervision and efficiently match the model of the object against large collections of images. In the learning stage, we show that by integrating our representation with Boosting the system is able to obtain a compact model that is represented by very few features, where each feature conveys key properties about the object's parts and their spatial arrangement. In the matching step, we propose direct procedures that exploit our representation for efficiently considering spatial coherence between the matching of local parts. Combined with an appropriate data organization such as Inverted Files, we show that thousands of images can be evaluated efficiently. The framework has been applied to different standard databases and we show that our results are favorably compared against state-of-the-art methods in both computational cost and accuracy.     

The implementation and evaluation of a recovery system for workflows

Workflow systems are popular in daily business processing. Since vulnerability cannot be totally removed from a workflow management system, successful attacks always happen and may inject malicious tasks or incorrect data into the workflow system. Moreover, legitimate tasks referring to the incorrect data will further corrupt more data objects in the system. As a result, the integrity level of the system can be seriously compromised. This problem cannot be efficiently solved by existing defense mechanisms, such as access control, intrusion detection, and checkpoints. In this paper, we propose a practical solution for on-line attack recovery of workflows. The recovery system discovers all damages caused by the malicious tasks and automatically repairs the damages based on data and control dependencies between workflow tasks. We describe fundamental theories for workflow attack recovery system. Based on these theories, we build a prototype system and develop the corresponding recovery algorithms. We evaluate the performance of the recovery system under different attacking densities, intrusion detection delays and arrival rates. The experimental results show that our system is practical.     

On the implementation of the Opus coordination language

Opus is a new programming language designed to assist in coordinating the execution of multiple, independent program modules. With the help of Opus, coarse grained task parallelism between data parallel modules can be expressed in a clean and structured way. In this paper we address the problems of how to build a compilation and runtime support system that can efficiently implement the Opus constructs. Our design considers the often‐conflicting goals of efficiency and modular construction through software re‐use. In particular, we present the system requirements for an efficient Opus implementation, the Opus runtime system, and describe how they work together to provide the underlying services that the Opus compiler needs for a broad class of machines. Copyright © 2000 John Wiley & Sons, Ltd.     

核聚类算法在入侵检测中的应用

提出了一种基于核的聚类算法,并将其应用到入侵检测中,构造了一种新的检测模型。通过利用Mercer核,我们把输入空间的样本映射到高维特征空间后,在特征空间中进行聚类。由于经过了核函数的映射,使原来没有显现的特征凸显出来,从而能够更好地聚类。而且在初始化聚类中心的选择上利用了数据分段的方法,该聚类方法在性能上比经典的聚类算法有较大的改进,具有更快的收敛速度以及更为准确的聚类。仿真试验的结果证实了该方法的可行性和有效性。     

Trie-join: a trie-based method for efficient string similarity joins

A string similarity join finds similar pairs between two collections of strings. Many applications, e.g., data integration and cleaning, can significantly benefit from an efficient string-similarity-join algorithm. In this paper, we study string similarity joins with edit-distance constraints. Existing methods usually employ a filter-and-refine framework and suffer from the following limitations: (1) They are inefficient for the data sets with short strings (the average string length is not larger than 30); (2) They involve large indexes; (3) They are expensive to support dynamic update of data sets. To address these problems, we propose a novel method called trie-join, which can generate results efficiently with small indexes. We use a trie structure to index the strings and utilize the trie structure to efficiently find similar string pairs based on subtrie pruning. We devise efficient trie-join algorithms and pruning techniques to achieve high performance. Our method can be easily extended to support dynamic update of data sets efficiently. We conducted extensive experiments on four real data sets. Experimental results show that our algorithms outperform state-of-the-art methods by an order of magnitude on the data sets with short strings.     

Vectorizing disks blocks for efficient storage system via deep learning

Efficient storage systems come from the intelligent management of the data units, i.e., disk blocks in local file system level. Block correlations represent the semantic patterns in storage systems. These correlations can be exploited for data caching, pre-fetching, layout optimization, I/O scheduling, etc. to finally realize an efficient storage system. In this paper, we introduce Block2Vec, a deep learning based strategy to mine the block correlations in storage systems. The core idea of Block2Vec is twofold. First, it proposes a new way to abstract blocks, which are considered as multi-dimensional vectors instead of traditional block Ids. In this way, we are able to capture similarity between blocks through the distances of their vectors. Second, based on vector representation of blocks, it further trains a deep neural network to learn the best vector assignment for each block. We leverage the recently advanced word embedding technique in natural language processing to efficiently train the neural network. To demonstrate the effectiveness of Block2Vec, we design a demonstrative block prediction algorithm based on mined correlations. Empirical comparison based on the simulation of real system traces shows that Block2Vec is capable of mining block-level correlations efficiently and accurately. This research and trial show that the deep learning strategy is a promising direction in optimizing storage system performance.     

一个基于B/S结构小型图书资料管理系统的设计和实现

本文涉及的图书资料系统是基于B／S结构的管理信息系统,针对小型机构的特点,在局域网环境下,以自助方式实现图书资料的登记和借还操作并支持电子邮件方式实现系统的增量备份,方便、快捷地完成图书的日常管理。主要介绍系统的设计思想和实现要点。     

在角色定权框架下实现能力机制

本文基于Kylin操作系统的角色定权框架完整地设计实现了遵循Posix1003．1e规范的能力机制，并且引入角色能力和用户能力的概念。利用该机制，我们可以在系统上有效地实施最小特权，包括分权、控制setuid和setgid程序、限制守护程序等。这样，系统中不再存在超级用户，其功能被划分到多个管理员用户之中；系统中每个进程都仅仅具有
有完成其任务所必需的特权，有效地阻止了滥用特权，大大提高了系统的安全。     

DCDedupe: Selective Deduplication and Delta Compression with Effective Routing for Distributed Storage

Data deduplication has been an essential part of storage systems for big data. Traditional compare-by-hash (CBH) deduplication does not fully address the challenges for similar files with small changes. Delta compression can be complementary to further optimize the storage efficiency. In this paper, we designed and implemented a distributed storage system called DCDedupe that efficiently and intelligently use delta compression or deduplication to improve storage efficiency based on characteristics of data. Unlike prior studies, this system works well when the data locality is weak or even barely exists. In DCDedupe, we propose a pre-processing step to identify content similarity and data chunks are classified into different categories. Then, the appropriate routing algorithm ensures the data chunks are sent to the right target storage nodes in the distributed system to boost the storage efficiency. Our evaluation shows that generally storage space saving by DCDedupe outweighs the performance penalties. In some use cases, DCDeupe may become meaningful to trade off some throughput with optimized storage costs. The overheads to Input/Output (IO) operation and memory usage have also been studied with design recommendations.