Data-driven hair segmentation with isomorphic manifold inference

Hair segmentation is challenging due to the diverse appearance, irregular region boundary and the influence of complex background. To deal with this problem, we propose a novel data-driven method, named Isomorphic Manifold Inference (IMI). The IMI method assumes the coarse probability map and the binary segmentation map as a couple of isomorphic manifolds and tries to learn hair specific priors from manually labeled training images. For an input image, firstly, the method calculates a coarse probability map. Then it exploits regression techniques to obtain the relationship between the coarse probability map of the test image and those of training images. Finally, this relationship, i.e., a coefficient set, is transferred to the binary segmentation maps and a soft segmentation of the test image will be achieved by a linear combination of those binary maps. Further, we employ this soft segmentation as a shape cue and integrate it with color and texture cues into a unified segmentation framework. A better segmentation is achieved by the Graph Cuts optimization. Extensive experiments are conducted to validate effectiveness of the IMI method, compare contributions of different cues and investigate the generalization of IMI method. The results strongly encourage our method.     

基于损益云模型的制造车间重调度决策方法

针对制造车间重调度触发机制问题,建立了制造车间重调度损益函数,揭示了生产车间重调度过程损失及增益的变化规律.引入云理论测度重调度损益的不确定性,使用逆向云算法计算重调度增益云和损失云的数字特征,根据云形态预测重调度损益变化趋势.提出一种基于损益云模型的重调度决策方法以判断是否需要重调度,并利用最佳损益比甄选预调度方案以兼顾生产系统的稳定性和有效性.最后,通过实例验证了该方法的合理性和实用性.     

Incremental inconsistency detection with low memory overhead

Ensuring models’ consistency is a key concern when using a model‐based development approach. Therefore, model inconsistency detection has received significant attention over the last years. To be useful, inconsistency detection has to be sound, efficient, and scalable. Incremental detection is one way to achieve efficiency in the presence of large models. In most of the existing approaches, incrementalization is carried out at the expense of the memory consumption that becomes proportional to the model size and the number of consistency rules. In this paper, we propose a new incremental inconsistency detection approach that only consumes a small and model size‐independent amount of memory. It will therefore scale better to projects using large models and many consistency rules. Copyright © 2012 John Wiley & Sons, Ltd.     

A model driven architecture for the development of smart card software

Smart cards are portable integrated devices that store and process data. Speed, security and portability properties enable smart cards to have a widespread usage in various fields including telecommunication, transportation and the credit card industry. However, the development of smart card applications is a difficult task due to hardware and software constraints. The necessity of the knowledge of both a very low-level communication protocol and a specific hardware causes smart card software development to be a big challenge for the developers. Written codes tend to be error-prone and hard to debug because of the limited memory resources. Hence, in this study, we introduce a model driven architecture which aims to facilitate smart card software development by both providing an easy design of smart card systems and automatic generation of the required smart card software from the system models. Differentiating from the previous work, the study in here contributes to the field by both providing various smart card metamodels in different abstraction layers and defines model-to-model transformations between the instances of these metamodels in order to support the realization of the same system on different smart card platforms. Applicability of the proposed methodology is shown for rapid and efficient application development in two major smart card frameworks: Java Card and ZeitControl Basic Card. Lessons learned during the industrial usage of the architecture are also reported in the paper. Finally, we discuss how the components of the architecture can be integrated in order to provide a domain-specific language for smart card software.     

Java课程教学改革与实践

针对Java课程教学特点及传统教学中存在的问题,对课程教学内容的整合、课堂教学和实践教学的组织、教学方法的综合运用、课程实训方法的选择与实施,以及考核方案的设定等环节进行了教学改革。实践表明,这些改革对提高学生的实践能力和就业竞争能力有一定的帮助。     

“网络营销”课程改革实施方案

"网络营销"课程知识体系须随着互联网应用的出现和普及而逐渐得到完善,为此提出了该课程的教学改革实施方案。网络营销课程的教学始终要以实践应用为主导,在教学过程中采用案例教学、任务驱动、结合竞赛、学科互动、团队合作等方法。考核应采用灵活分配学分比例的办法。最终使得学生能够将课堂上的知识灵活地运用到工作当中。     

Windows Server操作系统课程教学模式改革探索

从Windows Server操作系统课程的教学现状出发,提出了在Windows Server操作系统课程的教学中引入项目驱动教学模式及设计原则,并阐述了此模式下具体的教学环节设计,为Windows Server操作系统课程的教学在计算机网络技术专业技能训练与职业能力培养方面提供了新的视角,具有一定的实用和推广意义。     

基于RTX的工程飞行模拟器数据采集与存储系统设计

在一些仿真应用中,由于Windows操作系统较差的实时性,从而给许多半物理仿真试验带来了一些不确定的因素.在充分论证的基础上,引入了Ardence公司的基于Windows的硬实时解决方案RTX（Real-Time eXtension）,并结合某型号飞行器仿真课题总结出了Windows环境下RTX实时应用系统的设计方法与开发流程以及RTX下硬件板卡驱动程序的编写,最后将这种设计方法成功应用在某型号飞行器飞行仿真系统中.实验证明该仿真系统是可靠的、稳定的,整个实验过程中飞行器的飞行数据没有丢失,仿真周期确定,结果正确,RTX仿真程序能够满足系统对实时性和确定性的要求.     

数据结构课程教学改革探讨

数据结构是计算机专业的核心课程,对计算机学科起到承前启后的作用,而数据结构的抽象性,使学生掌握其课程内容感到困难,为了激发学生的学习兴趣,更好地掌握课程的理论知识,提高算法的设计能力和分析能力,本文针对数据结构课程教学现状和对存在的问题,就教学内容、教学方法和手段提出几点改革。     

基于Java电化教学中项目驱动教学法的应用

本文提出了以电化教学环境为平台,运用项目驱动教学法进行Java语言的教学,分析并论述了这种教学法在教学过程中的实现思路及实施过程。教学实践表明,本教学方法激发了学生学习的积极性,培养了学生分析问题和解决问题的实际应用能力。