基于长尾理论的航企营销新视野

现阶段,网络技术发展日新月异,航空公司的传统营销模式面临着越来越严峻的挑战,而"长尾理论"则为航空公司市场营销打开了新思路,开拓了新视野。本文将长尾理论应用到网络营销策略中,为航空公司面对更加多元化、利基化的市场趋势寻求新的视野。     

从需求层次理论看如何创建“阳光1+”空管特色班组

马斯洛的需求层次理论对完善激励机制,创建特色班组具有重要的指导意义。本文依照马斯洛的需求层次理论,对一线管制员思想及需求状况进行问卷调查,根据调查数据分析,得到了一线员工的心理特点,进而结合空管班组建设以及空管文化建设的要求,提出应从四个方面入手,打造"安全之家"、"学习之家"、"和谐之家"、"民主之家"的"家"文化,创建"阳光1+"空管特色班组。     

新一代小型运载火箭三子级热试车成功

7月11日,由中国航天科技集团公司八院抓总研制、805所抓总设计的新一代小型运载火箭三子级热试车在六院101所取得圆满成功。此次热试车主要考核三子级动力系统设计的正确性、合理性和协调性,尽量模拟发射场流程,暴露箭上、地面设计和操作中的薄弱环节,为后续设计改进提供依据。新一代小型运载火箭     

民用机场地面服务范围的界定——基于组合权重法和断裂点理论

鉴于机场地面服务范围是影响民用机场合理布局的主要因素之一,运用断裂点理论、熵权法、组合权重法,得到基于场强的机场地面辐射半径计算公式,提出确定经济区域内机场地面服务范围的思路和方法,并通过成渝经济区机场实际算例来论证成渝经济区机场布局的合理性,结论是近期内乐山机场不适宜扩建.     

钯改性镍基高温合金铝化物涂层的组织和热腐蚀性能

以M38合金作基材，先电镀Pd-20wt%Ni合金，再采用传统低温高活性(LTHA)固渗铝工艺，用NaCl作活性剂渗铝制得Pd-Ni-Al涂层。XRD和EPMA分析结果表明，涂层为固溶Ni的β-PdAl单相结构；在900℃Na     

航天飞行器热防护涂层研究进展

近年来,新研航天飞行器气动热环境往往具有高焓、高热流密度和长时间加热等特点,这促使防热材料的研制朝着低密度、高抗烧蚀、优良隔热等性能的方向发展。受自身材料类型的限制,无论烧蚀型(树脂基)或非烧蚀型(陶瓷基)防热材料都可以通过对烧蚀表面进行涂层处理的手段达到弥补材料性能短板,提高使用性能的目的。本文试图梳理、总结国内外近年来热防护系统用涂层材料的发展状况,探讨各自的优势和缺点,并提出了针对树脂基复合材料热防护涂层可能趋势的推测。     

FAST FEATURE RANKING AND ITS APPLICATION TO FACE RECOGNITION

A fast feature ranking algorithm for classification in the presence of high dimensionahty and small sample size is proposed. The basic idea is that the important features force the data points of the same class to maintain their intrinsic neighbor relations, whereas neighboring points of different classes are no longer to stick to one an- other. Applying this assumption, an optimization problem weighting each feature is derived. The algorithm does not involve the dense matrix eigen-decomposition which can be computationally expensive in time. Extensive exper- iments are conducted to validate the significance of selected features using the Yale, Extended YaleB and PIE data- sets. The thorough evaluation shows that, using one-nearest neighbor classifier, the recognition rates using 100-- 500 leading features selected by the algorithm distinctively outperform those with features selected by the baseline feature selection algorithms, while using support vector machine features selected by the algorithm show less prominent improvement. Moreover, the experiments demonstrate that the proposed algorithm is particularly effi- cient for multi-class face recognition problem.