基于电驻波的锚杆长度无损测量方法

针对现有锚杆无损检测方法存在振动波与锚杆的耦合问题,提出了一种基于电驻波的锚杆长度无损测量方法。该方法基于传输线和天线等效原理,采用传输线上的驻波理论来测量金属锚杆长度,即扫描输入信号频率,当锚杆始端第1次出现电压最小值时,认为对应的信号波长为锚杆长度的4倍。实测结果表明,该方法耦合方便,测量效果好。     

移动互联网对电信业务支撑系统的影响研究

基于移动互联网的特点对电信业务支撑系统进行研究,通过分析业务支撑系统的业务模型和功能,阐述了移动互联网对电信业务支撑系统带来的影响以及应对措施。     

基于遗传算法的航空弹药保障人员优化配置

航空弹药保障任务中人员合理高效的配置是一个多约束条件下的组合优化问题。在建立保障人员配置模型的基础上,通过操作因子对模型的合理描述,应用遗传算法对模型进行优化求解,得到合理的保障人员数量和最优的保障人员配置方案。实例仿真验证了模型的正确性及算法的有效性。     

基于非对称打包和FSVM的图像检索

在图像检索的相关反馈中,引入支持向量机分类方法虽可以提升图像的检索性能,但是传统的支持向量机存在正样本数少、样本非对称、过学习和弱实时性的局限。针对上述问题,提出了一种基于非对称打包的FSVM算法。该算法首先对负样本进行非对称打包处理,最后结合模糊理论与SVM实现图像检索。Corel图片集上的实验表明,当正样本数较小时,该新算法的平均查准率-查全率要优于已有算法。     

基于谱直方图与支持向量机的人脸检测

提出了一种基于谱直方图和支持向量机的人脸检测算法。首先使用梯度滤波器、LoG滤波器和LBP算子计算图像的谱直方图,然后使用支持向量机进行分类。经实验表明,算法可以克服光照、姿势、表情、眼镜等干扰,并允许有局部遮挡,具有较强的鲁棒性。     

Using adaptive e-news to improve undergraduate programming courses with hybrid format

With the growing popularity of computers and the Internet, most teachers are taking advantage of Internet functions to assist in both teaching and student learning. However, students only login to the assisted learning system once or twice a week on average to surf for relevant references, participate in discussions, or hand in assignments, even though information and interaction are greatly enhanced by these online services. Therefore, increasing the transfer rate of useful information and interaction during the course period would help students learn. To this end, we designed an e-news delivery system that sends personalized e-mails to every student’s mailbox to make information access and peer interaction more convenient and efficient. Through the daily habit of using e-mail, students can receive the latest information, new instructions or peer discussions early. To encourage usage of the system, a user friendly interface was designed. The e-news system takes advantage of webpage hyperlinks to allow students to click on interesting topics while reading e-news; doing so will cause another window to pop-up with detailed content retrieved from the web-based learning system. A mouse-click allows students to login to the web-based learning system and extract the details of the chosen topic. To enhance the efficiency of e-news, learners’ needs and interests were taken into consideration when forming the e-news content. Students’ learning status and reading preferences are assessed to determine which information or content to include in the e-news. Experiment results reveal that e-news and personalized information greatly promote the reading of new information and participation in online activities.     

Factors impacting the perceived organizational support of IT employees

Organizations today face shortages of IT personnel. We investigated workplace factors in one state government in hope of identifying factors that influence perceived organizational support (POS) within an IT work environment. A combination of job characteristics (challenging job and perceived workload), job stressors (work exhaustion, role conflict, and role ambiguity), and the organization’s discretionary actions (pay-for-performance and mentoring opportunities) were measured and hierarchical regression was used to determine the relationships. Four control variables were also included (age, gender, organizational tenure, and professional versus administrator status). Role ambiguity, role conflict, work exhaustion, career mentoring, and pay-for-performance together explained 62% of the variance in the IT employees’ POS. Career mentoring and role ambiguity explained most of the variance.     

A case study validation of a knowledge-based approach for the selection of requirements engineering techniques

Requirements engineering (RE) is a critical phase in the software engineering process and plays a vital role in ensuring the overall quality of a software product. Recent research has shown that industry increasingly recognizes the importance of good RE practices and the use of appropriate RE techniques. However, due to the large number of RE techniques, requirements engineers find it challenging to select suitable techniques for a particular project. Unfortunately, technique selection based on personal experience has limitations with regards to the scope, effectiveness and suitability of the RE techniques for the project at hand. In this paper, a Knowledge-based Approach for the Selection of Requirements Engineering Techniques (KASRET) is proposed that helps during RE techniques selection. This approach has three major features. First, a library of requirements techniques was developed which includes detailed knowledge about RE techniques. Second, KASRET integrates advantages of different knowledge representation schemata and reasoning mechanisms. Thus, KASRET provides mechanisms for the management of knowledge about requirements techniques and support for RE process development. Third, as a major decision support mechanism, an objective function evaluates the overall ability and cost of RE techniques, which is helpful for the selection of RE techniques. This paper makes not only a contribution to RE but also to research and application of knowledge management and decision support in process development. A case study using an industrial project shows the support of KASRET for RE techniques selection.

基于LSSVM的木材干燥建模研究

针对木材干燥过程的强非线性特点,提出以最小二乘支持向量机LSSVM建立木材干燥基准模型.通过实验用小型木材干燥窑实际干燥过程中采集的数据作为训练样本进行仿真实验,结果表明基于LSSVM的木材干燥模型预测输出能够准确反映干燥过程木材含水率的变化,模型结构简单、预测精度高、泛化能力强,验证了LSSVM对木材干燥过程建模是一种可行而有效的方法.     

AptaCDSS-E: A classifier ensemble-based clinical decision support system for cardiovascular disease level prediction

Conventional clinical decision support systems are generally based on a single classifier or a simple combination of these models, showing moderate performance. In this paper, we propose a classifier ensemble-based method for supporting the diagnosis of cardiovascular disease (CVD) based on aptamer chips. This AptaCDSS-E system overcomes conventional performance limitations by utilizing ensembles of different classifiers. Recent surveys show that CVD is one of the leading causes of death and that significant life savings can be achieved if precise diagnosis can be made. For CVD diagnosis, our system combines a set of four different classifiers with ensembles. Support vector machines and neural networks are adopted as base classifiers. Decision trees and Bayesian networks are also adopted to augment the system. Four aptamer-based biochip data sets including CVD data containing 66 samples were used to train and test the system. Three other supplementary data sets are used to alleviate data insufficiency. We investigated the effectiveness of the ensemble-based system with several different aggregation approaches by comparing the results with single classifier-based models. The prediction performance of the AptaCDSS-E system was assessed with a cross-validation test. The experimental results show that our system achieves high diagnosis accuracy (>94%) and comparably small prediction difference intervals (<6%), proving its usefulness in the clinical decision process of disease diagnosis. Additionally, 10 possible biomarkers are found for further investigation.