Semi-supervised gear fault diagnosis using raw vibration signal based on deep learning

In aerospace industry, gears are the most common parts of a mechanical transmission system. Gear pitting faults could cause the transmission system to crash and give rise to safety disaster. It is always a challenging problem to diagnose the gear pitting condition directly through the raw signal of vibration. In this paper, a novel method named augmented deep sparse autoencoder (ADSAE) is proposed. The method can be used to diagnose the gear pitting fault with relatively few raw vibration signal data. This method is mainly based on the theory of pitting fault diagnosis and creatively combines with both data augmentation ideology and the deep sparse autoencoder algorithm for the fault diagnosis of gear wear. The effectiveness of the proposed method is validated by experiments of six types of gear pitting conditions. The results show that the ADSAE method can effectively increase the network generalization ability and robustness with very high accuracy. This method can effectively diagnose different gear pitting conditions and show the obvious trend according to the severity of gear wear faults. The results obtained by the ADSAE method proposed in this paper are compared with those obtained by other common deep learning methods. This paper provides an important insight into the field of gear fault diagnosis based on deep learning and has a potential practical application value.     

Modeling the effects of green alternative on heat island mitigation of a meso level town,West Bengal,India

Urban heat island (UHI) is emerged as a ubiquitous phenomenon that affects the outdoor thermal comfort. Hence urban microclimatic studies using numeric simulation software to find out adaption strategies are attracting the great attention of researchers and policymaker. Number of scientists carried out their research on this topic using different tools and technique whereas ENVI-met model is the most widely used simulation tools. The present study intended to examine the implication of green infrastructure of the town on enhanced microclimatic condition and to evaluate the best suitable mitigation strategy modelling cool city with ENVI-met software (V4). Five greening condition such as existing UHI condition (C1), 100% greening of roof (C2), 100% greening of roof and walls (C3), 50% greening of roof and walls (C4) and: Plantation at suitable area with 50% greening of roof and walls (C5) have been taken into consideration for the microclimate simulation for all three selected sites. Among these five conditions, C3 for the open mid-rise and compact low-rise, C5 for the open low-rise are identified as one of the most suitable strategies which can reduce the air temperature of peak hours by 2.6 °C, 1.33 °C and 1.87 °C respectively. These models are validated by simple linear regression between simulated and existing air temperature in case of existing UHI condition (C1) and in all the cases coefficient of determination value is high such as for open mid-rise, compact low-rise and open low-rise, it is 0.92, 0.92 and 0.75 respectively. Therefore it can be concluded that the application of those strategies can improve the urban thermal environment as well as the outdoor thermal comfort of English bazaar Town and its surroundings.     

基于梯度提升决策树的量子科学实验卫星光学实验预测

量子科学实验卫星在轨运行期间完成4种光学实验，地面监测人员通过遥测参数阈值判断卫星是否进行光学实验、实验类型及实验结果.这种方法需要预先设定大量阈值，并且这些阈值需要根据在轨卫星重新设定，可扩展性较差.针对以上问题，提出一种基于机器学习的光学实验判别方法，将量子科学实验卫星的光学实验监测任务抽象为机器学习中的多元分类问题，构建分类模型，利用量子科学实验卫星的真实历史遥测数据对模型进行训练，并通过真实实验计划对训练得到的模型进行验证.实验结果表明，本文提出的方法在没有专家先验知识的前提下，判别准确率达到99%，可用于量子科学实验卫星光学实验的实时监测任务.提出的基于机器学习的判别方法具有较强的可扩展性，可应用于卫星在轨运行的其他监测任务.      

河北省高校农村生源学生思想现状调查研究

河北省农村人口占总人口的79％，是一个比较典型的农业大省。农村生源大学生，是河北省在校大学生的重要组成部分，占生源的最大比例。大学生是宝贵的人才资源，他们的素质尤其是思想政治素质如何，他们能否成为社会主义事业的合格建设者和可靠接班人，是一个非常重要的问题。因而，科学分析和把握他们的思想政治状况，将会增强我们把握大学生思想脉搏的准确性和说服力。为此笔者选取了河北省八个地市的不同高校，进行了问卷调查、座谈和访谈。对河北省农村生源大学生的思想现状进行了调查。通过统计软件的录入和整理，统计结果比较真实地显示了当前河北省农村生源大学生的思想现状。     

专科英语、本科英语及成人专科英语的定位和相互关系

专科英语在理论上被界定为专门用途英语(ESP)分支下,介于学术英语(EAP)与职业英语(EOP)之间的业务用途英语(EPP)。而本科(大学)英语在理论体系上属于普通英语(GE)。本文从ESP理论的角度探讨了专科英语,本科英语及成人专科英语的定位和相互关系。     

HML系统在商店偏好决策模型中的应用研究

对混合式机器学习系统（HML）进行了全面系统的介绍，并将其运用于服装消费商店偏好决策行为的研究中。采用上海统计局家庭调查网络，对300户家庭进行抽样分析后发现，服装消费商店偏好的主要影响因素是地区、季节、丈夫和妻子的学历、职业以及子女性别。在此基础上，将HML分析所得的结论与传统的研究方法和结果进行了系统比较：从方法上来看，因为属性变量包含间断变量和连续变量两种，因此传统统计分析要运用两种不同的检测方法对影响因素的相关性作出判断，结果需要经过统计学分析，才能得到结论；而HML分析结果比较直观和简单，便于理解。     

以培养适用型人才为宗旨建立新型的校外学习中心管理体系

校外学习中心是远程教学和远程学习的活动基地。校外学习中心必须能适应市场经济形势需求,以培养适用型人才为宗旨,不断完善自身机制,实行规范化管理,不断提高教学管理工作水平,建立起新型的管理体系。     

高职院校校园文化活动创新原则与方法探讨

高等职业教育是高等教育的重要类型,校园文化是学校精神文明建设的重要内容。通过对高校目前校园文化活动存在的共性问题和高职院校的个性分析,提出了高职院校校园文化活动创新的基本原则、主要内容和方法。     

浅谈发挥低年级学生学习英语的积极性

英语教学，特别是低年级英语教学既需要教师的积极性，更需要学生的积极性，二者缺一不可。要想发挥学生的学习积极性，首先应发挥英语自身的魅力，启发学生的积极性；其次，以情感促教学调动学生的积极性；再者，采取有效措施巩固学生的积极性。     

培养学生英语口语交际能力的策略探讨

文章就大学生英语口语交际能力存在的问题进行了分析,探讨如何提高英语口语交际能力的策略。