基于G/S模式的空间信息云服务架构研究

针对多系统间的数据交换、共享问题,尤其在大数据时代,海量的数据增长给数据的管理带来的挑战,提出了一种基于G/S(General-Browser/ Service-Cloud,G/S)模式的空间信息云服务架构——面向数据的架构(Data Oriented Architecture,DOA)。以数据为核心,以数据标识为主线,通过数据注册中心和数据交换规范XXML(Specific Industry Markup Language),对海量异构数据进行存储、计算和管理,实现多系统间的数据共享、访问和协同。通过在地质灾害监测预警方面的应用,证明了该架构作为信息技术的系统构建方法在大数据时代独特的优势。     

一种车用单刀双掷开关的质检仪器研发

介绍了一种车用单刀双掷开关质量检测仪器的研发,包括检测系统的硬件设计、检测原理以及算法软件开发。首先,根据单刀双掷开关的工作原理及质量检测要求,开发了检测手指触觉压力的机械装置,以及控制开关通道切换的继电器控制电路;在此基础上,开发了相应的检测算法,可实现对开关切换状态、电压差以及手指触觉压力进行自动化的数据采集、分析、检测和判断,从而挑选出合格的开关;最后,设计了触屏人机交互界面,可实时显示产品检测的参数。该检测设备在某自动化生产线上经过近一年时间的检测验证,证明系统运行平稳,检测效果良好、误检率低,可以完全取代传统的人工检测方式,提高了开关的检测效率,降低了检测人员的工作量。     

Efficiency assessment of primary care providers: A conditional nonparametric approach

This paper uses a fully nonparametric approach to estimate efficiency measures for primary care units incorporating the effect of (exogenous) environmental factors. This methodology allows us to account for different types of variables (continuous and discrete) describing the main characteristics of patients served by those providers. In addition, we use an extension of this nonparametric approach to deal with the presence of undesirable outputs in data, represented by the rates of hospitalization for ambulatory care sensitive condition (ACSC). The empirical results show that all the exogenous variables considered have a significant and negative effect on efficiency estimates.     

An analysis of managerialism and performance in English and Welsh male prisons

This paper fills a noticeable gap in the current economic and penology literature by proposing new performance-enhancing policies based on an efficiency analysis of a sample of male prisons in England and Wales over the period 2009/10. In addition, we advance the empirical literature by integrating the managerialism of four strategic functions of prisons, employment and accommodation, capacity utilization, quality of life in prison and the rehabilitation and re-offending of prisoners. We find that by estimating multiple models focussing on these different areas some prisons are more efficient than other establishments. In terms of policy, it is therefore necessary to consider not just an overall performance metric for individual prisons, as currently undertaken annually by the UK Ministry of Justice, but to look into the administration and managerialism of their main functions in both a business and public policy perspective. Indeed, it is further necessary to view prisons together and not as single entities, so as to obtain a best practice frontier for the different operations that management undertakes in English and Welsh prisons.     

粉末微波滤波器的制备和特性

利用通电导线的电磁感应和铜粉的趋肤效应,试制了在超导量子计算实验系统中使用的粉末微波滤波器.滤波器的频率响应随频率升高而平缓下降,在1GHz左右衰减至-80dB.我们分析了滤波器的原理,并通过改变参数对滤波器性能的变化进行了详细的研究.     

复合材料失效判据有效性验证方法研究

为系统地验证复合材料失效判据计算精度和有效性范围,给出了4种材料体系、6种铺层形式的层合板在单轴、双轴载荷下的失效试验数据,用以评估复合材料失效判据在单向层合板失效包线、多向层合板初始失效包线、多向层合板最终失效包线、层合板变形及层合板的破坏特性等五个方面的预测能力。并根据验证方法和有效性评估策略对失效判据计算精度进行量化考核,给出了失效判据在五个层面上的计算精度。     

A Probabilistic Re-Intepretation of Confidence Scores in Multi-Exit Models

In this paper, we propose a new approach to train a deep neural network with multiple intermediate auxiliary classifiers, branching from it. These ‘multi-exits’ models can be used to reduce the inference time by performing early exit on the intermediate branches, if the confidence of the prediction is higher than a threshold. They rely on the assumption that not all the samples require the same amount of processing to yield a good prediction. In this paper, we propose a way to train jointly all the branches of a multi-exit model without hyper-parameters, by weighting the predictions from each branch with a trained confidence score. Each confidence score is an approximation of the real one produced by the branch, and it is calculated and regularized while training the rest of the model. We evaluate our proposal on a set of image classification benchmarks, using different neural models and early-exit stopping criteria.     

Code2vect: An efficient heterogenous data classifier and nonlinear regression technique

The aim of this paper is to present a new classification and regression algorithm based on Artificial Intelligence. The main feature of this algorithm, which will be called Code2Vect, is the nature of the data to treat: qualitative or quantitative and continuous or discrete. Contrary to other artificial intelligence techniques based on the “Big-Data,” this new approach will enable working with a reduced amount of data, within the so-called “Smart Data” paradigm. Moreover, the main purpose of this algorithm is to enable the representation of high-dimensional data and more specifically grouping and visualizing this data according to a given target. For that purpose, the data will be projected into a vectorial space equipped with an appropriate metric, able to group data according to their affinity (with respect to a given output of interest). Furthermore, another application of this algorithm lies on its prediction capability. As it occurs with most common data-mining techniques such as regression trees, by giving an input the output will be inferred, in this case considering the nature of the data formerly described. In order to illustrate its potentialities, two different applications will be addressed, one concerning the representation of high-dimensional and categorical data and another featuring the prediction capabilities of the algorithm.     

Data assimilation and pollution forecasting in Burgers' equation with model error function

This article presents a correction method for a better resolution of the problem of estimating and predicting pollution, governed by Burgers' equations. The originality of the method consists in the introduction of an error function into the system's equations of state to model uncertainty in the model. The initial conditions and diffusion coefficients, present in the equations for pollution and concentration, and also those in the model error equations, are estimated by solving a data assimilation problem. The efficiency of the correction method is compared with that produced by the traditional method without introduction of an error function.Three test cases are presented in this study in order to compare the performances of the proposed methods. In the first two tests, the reference is the analytical solution and the last test is formulated as part of the “twin experiment”.The numerical results obtained confirm the important role of the model error equation for improving the prediction capability of the system, in terms of both accuracy and speed of convergence.     

Geographic Characterization of Leccinum rugosiceps by Ultraviolet and Infrared Spectral Fusion

Leccinum rugosiceps is an edible mushroom belonging to genus Leccinum of Boletaceae. Its fruiting bodies are richer in nutrients than many vegetables and fruit. The model of support vector machine was established for the discrimination of L. rugosiceps from regions based on rapid and low-cost ultraviolet and infrared spectroscopies. The mid-level data fusion was performed by support vector machine. Compared to a single spectroscopic technique, mid-level data fusion provided higher accuracy by selecting the most significant variance from data matrixes based on partial least squares discriminant analysis. The accuracy of the classification of samples in the calibration and test sets were 85.00 and 94.74%, higher than separate measurements by ultraviolet or infrared spectroscopy. This approach has applications for authentication and quality assessment of L. rugosiceps.