基于支持向量机的捷联惯组历次测试数据建模预测

针对捷联惯组历次测试数据的小样本建模问题,在对测试数据进行了插值和相空间重构的基础上,提出了通过支持向量机建立测试数据的回归模型,用来预测捷联惯组历次测试数据的变化趋势.实例分析证明该方法能够准确反映捷联惯组历次测试数据的变化规律,具有一定的实际应用价值.     

一种新的车牌图像字符分割与识别算法

提出了一种新的字符分割算法,该算法利用由中间向两端搜索的方法确定字符的上下边界,且利用一维循环清零法及先验知识垂直分割得到单个的字符。实验结果表明,该算法较好的解决了以上问题,适用于各种质量的车牌图像,分割的正确率较高。     

Effect of prior cold work on age hardening of Cu–3Ti–1Cr alloy

The influence of 50%, 75% and 90% cold work on the age hardening behavior of Cu–3Ti–1Cr alloy has been investigated by hardness and tensile tests, and light optical and transmission electron microscopy. Hardness increased from 118 Hv in the solution-treated condition to 373 Hv after 90% cold work and peak aging. Cold deformation reduced the peak aging time and temperature of the alloy. The yield strength and ultimate tensile strength reached a maximum of 1090 and 1110 MPa, respectively, following 90% deformation and peak aging. The microstructure of the deformed alloy exhibited elongated grains and deformation twins. The maximum strength on peak aging was obtained due to precipitation of the ordered, metastable and coherent β′-Cu₄Ti phase, in addition to high dislocation density and deformation twins. Over-aging resulted in decreases in hardness and strength due to the formation of incoherent and equilibrium β-Cu₃Ti phase in the form of a cellular structure. However, the morphology of the discontinuous precipitation changed to a globular form on high deformation. The mechanical properties of Cu–3Ti–1Cr alloy are superior to those of Cu–2.7Ti, Cu–3Ti–1Cd and the commercial Cu–0.5Be–2.5Co alloys in the cold-worked and peak-aged condition.     

Can the use of cognitive and metacognitive self-regulated learning strategies be predicted by learners’ levels of prior knowledge in hypermedia-learning environments?

Research on self-regulated learning (SRL) in hypermedia-learning environments is a growing area of interest, and prior knowledge can influence how students interact with these systems. One hundred twelve (N = 112) undergraduate students’ interactions with MetaTutor, a multi-agent, hypermedia-based learning environment, were investigated, including how prior knowledge affected their use of SRL strategies. We expected that students with high prior knowledge would engage in significantly more cognitive and metacognitive SRL strategies, engage in different sequences of SRL strategies, spend more time engaging in SRL processes, and visit more pages that were relevant to their sub-goals than students with low prior knowledge. Results showed significant differences in the total use of SRL strategies between prior knowledge groups, and more specifically, revealed significant differences in the use of each metacognitive strategy (e.g., judgment of learning), but not each cognitive strategy (e.g., taking notes) between prior knowledge groups. Results also revealed different sequences of use of SRL strategies between prior knowledge groups, and that students spent different amounts of time engaging in SRL processes; however, all students visited similar numbers of relevant pages. These results have important implications on designing multi-agent, hypermedia environments; we can design pedagogical agents that adapt to students’ learning needs, based on their prior knowledge levels.     

Gatekeepers’ innovative use of IT: An absorptive capacity model at the unit level

This paper examines the potential role of gatekeepers’ innovative use of information technology (IT) in organizational units and its ability antecedents. Using absorptive capacity theory as our theoretical lens, we theorize a model of innovative use of IT at the unit level. The proposed model was empirically validated using a survey of 204 unit gatekeepers. This paper develops a preliminary model of innovative use of IT that can inform and guide future research, specifically focusing on the key role of gatekeepers and the abilities needed by users in this role that can help in using IT in an innovative manner.     

Bayesian methodology to estimate and update safety performance functions under limited data conditions: A sensitivity analysis

In road safety studies, decision makers must often cope with limited data conditions. In such circumstances, the maximum likelihood estimation (MLE), which relies on asymptotic theory, is unreliable and prone to bias. Moreover, it has been reported in the literature that (a) Bayesian estimates might be significantly biased when using non-informative prior distributions under limited data conditions, and that (b) the calibration of limited data is plausible when existing evidence in the form of proper priors is introduced into analyses. Although the Highway Safety Manual (2010) (HSM) and other research studies provide calibration and updating procedures, the data requirements can be very taxing. This paper presents a practical and sound Bayesian method to estimate and/or update safety performance function (SPF) parameters combining the information available from limited data with the SPF parameters reported in the HSM. The proposed Bayesian updating approach has the advantage of requiring fewer observations to get reliable estimates. This paper documents this procedure. The adopted technique is validated by conducting a sensitivity analysis through an extensive simulation study with 15 different models, which include various prior combinations. This sensitivity analysis contributes to our understanding of the comparative aspects of a large number of prior distributions. Furthermore, the proposed method contributes to unification of the Bayesian updating process for SPFs. The results demonstrate the accuracy of the developed methodology. Therefore, the suggested approach offers considerable promise as a methodological tool to estimate and/or update baseline SPFs and to evaluate the efficacy of road safety countermeasures under limited data conditions.     

流域规划优先保护生态目标识别与保护要求

对流域综合利用规划中生态保护优先、确保生态底线需要避让、重点保护、有效修复的流域环境敏感目标的识别与保护要求进行了讨论,可为流域综合利用规划和规划环境影响评价工作提供参考。     

“Objective” low informative priors for Bayesian inference from totally censored Gaussian data

Consider structural elements with random strength that after a suitable transformation has normal distribution with unknown mean μ and known or unknown standard deviation σ. By proof testing of n of these structural elements to a given load level it is observed that none of the elements fail. Given solely this test information the problem is that in order to state anything about either the value of μ when σ is known or about the values of μ and σ when both parameters are unknown, it is necessary to introduce some more information in the form of a suitable prior distribution of the parameters, that is, to use a Bayesian procedure with an informative prior. The paper considers the problem of defining such a prior in an axiomatic (“objective”) way without extending the information represented by the test results by more than very little extra information based on common physical sense. The solution suggested in the paper implies that the posterior distribution of the mean shifts towards larger values when the sample size n increases. However, convergence to a specific value is not obtained as long as no failures are observed among the tests. Moreover it turns out that the posterior distribution of the standard deviation is invariant to the sample size n, that is, no updating of the standard deviation is obtained as long as there are no failures among the tests.     

融合遥感先验信息的叶面积指数反演

借助植被辐射传输模型，利用遥感观测数据估算LAI是一种较为可靠和稳健的反演方法。然而，地表的复杂性、遥感观测的有限性以及自相关性导致遥感数据包含的信息量不足，不能完全支持LAI等地表参数的估算，易造成“病态”反演。在遥感反演过程中引入先验知识能够有效地解决该问题。研究基于遥感数据提取LAI先验信息，并将其用于代价函数的构建，利用PROSAIL辐射传输模型和遗传算法，分别在500 m和250 m尺度反演LAI。将高空间分辨率LAI分别升尺度到500 m和250 m，验证对应尺度LAI结果，评价引入先验信息对于提高LAI反演精度的作用。研究表明，引入先验信息有助于提高不同分辨率下LAI反演精度，且先验信息的质量一定程度上也影响着LAI反演结果。与未加入先验信息的LAI反演结果相比，以MODIS LAI产品作为先验信息反演的500 m尺度LAI结果精度R²由0.55提高至0.65，RMSE由1.29下降至0.38。在250 m尺度，以500 m LAI反演结果作为先验信息反演的叶面积指数，其精度优于以MODIS LAI产品为先验知识的估算结果，验证精度R²增加了0.08，RMSE减少了0.18。研究使用的先验信息主要来自遥感数据本身，没有地面实测数据的参与，在此基础上发展的多分辨率LAI反演方法具有估算大区域尺度LAI的应用潜力。     

Bayesian regression filters and the issue of priors

We propose a Bayesian framework for regression problems, which covers areas usually dealt with by function approximation. An online learning algorithm is derived which solves regression problems with a Kalman filter. Its solution always improves with increasing model complexity, without the risk of over-fitting. In the infinite dimension limit it approaches the true Bayesian posterior. The issues of prior selection and over-fitting are also discussed, showing that some of the commonly held beliefs are misleading. The practical implementation is summarised. Simulations using 13 popular publicly available data sets are used to demonstrate the method and highlight important issues concerning the choice of priors.