Inductive transfer with context-sensitive neural networks

Context-sensitive Multiple Task Learning, or csMTL, is presented as a method of inductive transfer which uses a single output neural network and additional contextual inputs for learning multiple tasks. Motivated by problems with the application of MTL networks to machine lifelong learning systems, csMTL encoding of multiple task examples was developed and found to improve predictive performance. As evidence, the csMTL method is tested on seven task domains and shown to produce hypotheses for primary tasks that are often better than standard MTL hypotheses when learning in the presence of related and unrelated tasks. We argue that the reason for this performance improvement is a reduction in the number of effective free parameters in the csMTL network brought about by the shared output node and weight update constraints due to the context inputs. An examination of IDT and SVM models developed from csMTL encoded data provides initial evidence that this improvement is not shared across all machine learning models.     

Learning to Locate Informative Features for Visual Identification

Object identification is a specialized type of recognition in which the category (e.g. cars) is known and the goal is to recognize an object’s exact identity (e.g. Bob’s BMW). Two special challenges characterize object identification. First, inter-object variation is often small (many cars look alike) and may be dwarfed by illumination or pose changes. Second, there may be many different instances of the category but few or just one positive “training” examples per object instance. Because variation among object instances may be small, a solution must locate possibly subtle object-specific salient features, like a door handle, while avoiding distracting ones such as specular highlights. With just one training example per object instance, however, standard modeling and feature selection techniques cannot be used. We describe an on-line algorithm that takes one image from a known category and builds an efficient “same” versus “different” classification cascade by predicting the most discriminative features for that object instance. Our method not only estimates the saliency and scoring function for each candidate feature, but also models the dependency between features, building an ordered sequence of discriminative features specific to the given image. Learned stopping thresholds make the identifier very efficient. To make this possible, category-specific characteristics are learned automatically in an off-line training procedure from labeled image pairs of the category. Our method, using the same algorithm for both cars and faces, outperforms a wide variety of other methods.     

基于遗传算法的通道上停车换乘量确定方法

根据外来客流进入城市中心的出行过程与机理,最小化所有客流出行(自城市外围到达市中心区域)的出行费用(私人车辆行驶时间与公交行驶时间总行程时间、换乘时间、停车费用等),建立、确定了各停车换乘点(P+R)处合理换乘量的双层规划模型,为各P+R的停车换乘诱导提供了依据,并给出模型的遗传算法求解方法。仿真示例证明了该方法的有效性。     

Usb复合设备实现Usb完全覆盖测试的探讨

Usb设备控制芯片或者控制器硬件模块开发的完成意味着测试的开始,通过对Usb测试流程的分析,提出了采用Usb多接口复合设备对Usb设备功能进行完全覆盖测试的方案,简化了测试的结构和流程,提高了Usb测试的效率和可实施性。     

递归程序设计方法的分析探讨

递归是设计程序的一种重要方法,文中对递归的定义及递归的基本条件进行了分析,给出了递归程序设计的基本方法,并结合数据结构中二叉树遍历和单链表结点输出的实例进行了验证,最后对递归程序进行了深度分析,说明了递归是一种有效的程序设计方法。     

高密度机柜散热策略分析与实现

随着数据中心机房内高密度机柜的不断增加,设备的集成度越来越高,散热问题已成为数据中心机房管理的主要难题。该文中讲述了五种应对高密度机柜散热问题的策略,总结了应用高密度计算的最佳散热策略,最后介绍了提高冷却效率和冷却能力以及提高现有数据中心密度的具体措施。     

谐振腔蒸汽湿度测量系统的补偿研究

在微波单腔测量流动湿蒸汽的系统中,影响蒸汽湿度测量准确性的因素比较多。分析了谐振腔热膨胀引起的频率漂移、水膜厚度引起谐振频率的变化以及频率测量环节引起的测量误差,并分别介绍了相应的补偿措施。通过对各环节的补偿,可以有效地提高蒸汽湿度的测量精度。     

直流炉单相介质换热器建模与仿真

本文对火电厂直流炉单相介质换热器的集总参数模型的计算及建立过程做了详细论述。并在模型中增加了辐射换热系数及对数温压计算,使模型精度提高,刚度下降。仿真结果更加接近实际情况。     

基于FPGA的一类非线性系统模糊控制的改进

针对温控系统不确定的非线性控制,提出了在FPGA硬件平台上分析和设计一种改进的模糊控制器,在传统的模糊控制器的基础上,引入误差量化因子自调整模块,从而减小模糊控制器的稳态误差,实现快速响应和小的超调.     

用Mathematica解热量传递过程中的数学问题

本文建议用数学软件Mathematica计算热量传递过程,包括用符号运算功能推导保温层临界直径,用曲线拟合法处理传热实验数据,用积分法求取传热系数为温度函数时的换热面积,用Solve等命令求解换热和保温过程所产生的非线性方程,用DSolve命令求解非稳态传热过程产生的微分方程等等。同时绘制出有关函数图形,准确、形象地展示计算过程和结果,方便教师讲解和学生理解,避免复杂、繁琐、耗时的手工计算,提高学生利用计算机解决热量传递问题的能力。