Face recognition via adaptive image combination

Dimension reduction and manifold learning are the two most popular feature extraction methods. The two methods focus on spatial locality as a guiding principle to find a low-dimensional basis for describing high-dimensional data, but no bases or features are more spatially localized than the original image pixels. So, adaptive image combination is presented to represent a class by a combined sample. The combined sample is a linear combination of original samples in the same class. Adaptive image combination (AIC) find the best combination coefficients by minimizing the intrapersonal distance and maximizing the interpersonal distance. Experimental results show that AIC is effective.     

Parameter transfer of common-metric attributes in choice analysis: implications for willingness to pay

There is a growing literature that promotes the presence of a mix of compensatory and semi-compensatory processing strategies in the way that individuals evaluate packages of attributes in real or hypothetical markets, and make choices. This paper proposes a specification for the utility form in a choice model to test if, given a pair of attributes with a common-metric (e.g., components of travel time or cost), the attribute with the dominating level defines the marginal (dis)utility that is assigned to both attributes. We refer to this processing strategy as a parameter transfer rule. We use a stated choice data set, in the context of car driving individuals choosing between tolled and non-tolled routes, to estimate a mixed logit model which incorporates the presence of the parameter transfer rule and the conventional fully compensatory rule, both existing up to a probability. We find that if this parameter transfer heuristic is part of the mix, the WTP is more than 30% higher, on average, than when only a fully compensatory rule is imposed. We also contrast the parameter transfer rule with other semi-compensatory heuristics which have been investigated in other papers, and show that the finding adds further support to the accumulating evidence that a semi-compensatory attribute processing rules tend to result in higher mean WTP estimates compared to the fully compensatory attribute processing rule.     

浙赣线提速路基基床承载力评估及加固措施

首先根据路基不同深度处的动应力衰减规律和浙赣线既有路基状况,提出了路床土强度控制条件为:基床表层0~0.3m和0.3~0.6m应分别满足160kPa和110kPa的承载力要求,并采用轻型动力触探(N10)对基床土承载力进行了检测和评估,结果表明,不满足基床土强度控制条件地段约占非改线全段的20%。其次,根据基床土承载力检测结果和基床土填料情况,提出了土工布封闭结合土工格室、换填的加固方法,而换填厚度取决于承载力大小和选取用的格室高度,为浙赣线200km/h提速改造提供了依据。     

大型落地镗铣床滑枕的有限元分析

通过三维软件建立了大型数控落地镗铣床滑枕的三维模型,利用有限元软件进行静力分析,计算了滑枕的应力和变形情况.通过模态分析,计算了滑枕1至4阶的固有频率和振型,得到滑枕动态特性,进而指导滑枕设计与优化.     

科技引领生态景观大道建设 创新打造未来城市名片

介绍了天津市滨海新区天津大道"绿色、生态、景观、环保、节能、和谐"的设计主题。从该工程的规划、设计,路基处理,路面材料,边沟设置,景观设计等方面阐述了工程的创新思想,并介绍了该工程绿色生态型城市快速路集成技术、城市快速路生态化设计技术集成、天津滨海新区城市快速路软土地基处理等科研课题的内容。     

高速摄像结合虚拟仪器测量振动及其AVD方法研究

一种高效率、高精度的振动测量和分析方法,该方法不仅可以获得振动参数,还能获得加速度（A）、速度（V）和位移（D）（本文简称AVD值）。与传统方法不同,该方法是基于虚拟仪器及其计算机运行环境的一种非接触测量方法,通过测量与虚拟仪器实现对振动图象的数字化处理和通道扩展,测量完成后除获得震动参量外还可直接获得相关AVD值。通过实验室验证其有效性并准备用于某种发射装置振动特征值的测量。     

航海模拟器中船舶进港训练评估系统的研究

为了提高航海训练评估的效率,减少主观因素对评估结果的影响,实现自动评估成为当前热点。在调查大量的学员实际训练和考试评估数据基础上,利用概率论中的非参数假设检验方法,对学员的操纵误差予以分析,得到学员的误差分布函数,并结合教师在考试评估中评定成绩的概率,计算出评估的等级区间。最后利用加权平均法计算出学生的综合成绩,采用VC＋＋6．0实现的系统在航海模拟器上测试,得到了比较理想结果。     

The precautionary principle in maritime affairs

Since the 1980s, the precautionary principle has been drawing more and more international attention. This became particularly clear during the series of Conferences on the Protection of the North Sea, which addressed marine environmental protection, and initiated the application of the principle into maritime affairs. Although a unified concept of the precautionary principle is lacking, some typical formulations have been concluded in regional treaties and international documents, and components of the principle can be summarised (Part I and Part II). The precautionary principle has been adopted by international documents involving different fields of law of the sea, and been addressed by international courts and tribunals (Part III). While the precautionary principle is still not part of customary international law (Part IV), its increasing level of worldwide acceptance suggests that it will become a customary international law in the future.     

Large-scale application of MILATRAS: case study of the Toronto transit network

This paper documents the efforts to operationalize the conceptual framework of MIcrosimulation Learning-based Approach to TRansit Assignment (MILATRAS) and its component models of departure time and path choices. It presents a large-scale real-world application, namely the multi-modal transit network of Toronto which is operated by the Toronto Transit Commission (TTC). This large-scale network is represented by over 500 branches with more than 10,000 stops. About 332,000 passenger-agents are modelled to represent the demand for the TTC in the AM peak period. A learning-based departure time and path choice model was adopted using the concept of mental models for the modelling of the transit assignment problem. The choice model parameters were calibrated such that the entropy of the simulated route loads was optimized with reference to the observed route loads, and validated with individual choices. A Parallel Genetic Algorithm engine was used for the parameter calibration process. The modelled route loads, based on the calibrated parameters, greatly approximate the distribution underlying the observed loads. 75% of the exact sequence of transfer point choices were correctly predicted by the off-stop/on-stop choice mechanism. The model predictability of the exact sequence of route transfers was about 60%. In this application, transit passengers were assumed to plan their transit trip based on their experience with the transportation network; with no prior (or perfect) knowledge of service performance.     

Simulating the impacts of household travel on greenhouse gas emissions,urban air quality,and population exposure

This paper establishes a link between an activity-based model for the Greater Toronto Area (GTA), dynamic traffic assignment, emission modelling, and air quality simulation. This provides agent-based output that allows vehicle emissions to be tracked back to individuals and households who are producing them. In addition, roadway emissions are dispersed and the resulting ambient air concentrations are linked with individual time-activity patterns in order to assess population exposure to air pollution. This framework is applied to evaluate the effects of a range of policy interventions and 2031 scenarios on the generation of vehicle emissions and greenhouse gases in the GTA. Results show that the predicted increase of approximately 2.6 million people and 1.3 million jobs in the region by 2031 compared to 2001 levels poses a major challenge in achieving meaningful reductions in GHGs and air pollution.