Optimization and regularization of complex task decomposition for blind removal of multi-factor degradation

Most existing image restoration methods based on deep neural networks are developed for images which only degraded by a single degradation mode and imaging under an ideal condition. They cannot be directly used to restore the images degraded by multi-factor coupling. A complex task decomposition regularization optimization strategy (TDROS) is proposed to solve the problem. The restoration of images degraded by multi-factor coupling is a complex task that can be solved by separating these multiple factors, that is, breaking the complex task into numbers of simpler tasks to make the entire complex problem be overcome more easily. Motivated by this idea, the TDROS decomposes the complex task of image restoration into two sub-task: the potential task constrained by regularization and the main task for reconstructing high-definition images. In TDROS, the front of the neural network is focused on the restoration of images degraded by additive noise, while the other part of the network is focused mainly on the restoration of images degraded by blur. We applied the TDROS to an 11-layer convolutional neural network (CNN) and compared it with initial CNNs from the aspects of restoration accuracy and generalization ability. Based on these results, we used TDROS to design a novel network model for the restoration of atmospheric turbulence-degraded images. The experimental results demonstrate that the proposed TDROS can improve the generalization ability of the existing network more effectively than current popular methods, offering a better solution for the problem of severely degraded image restoration. Moreover, the TDROS concept provides a flexible framework for low-level visual complex tasks and can be easily incorporated into existing CNNs.     

Multisensor data fusion via Gaussian process models for dimensional and geometric verification

An increasing amount of commercial measurement instruments implementing a wide range of measurement technologies is rapidly becoming available for dimensional and geometric verification. Multiple solutions are often acquired within the shop-floor with the aim of providing alternatives to cover a wider array of measurement needs, thus overcoming the limitations of individual instruments and technologies.In such scenarios, multisensor data fusion aims at going one step further by seeking original and different ways to analyze and combine multiple measurement datasets taken from the same measurand, in order to produce synergistic effects and ultimately obtain overall better measurement results.In this work an original approach to multisensor data fusion is presented, based on the development of Gaussian process models (the technique also known as kriging), starting from point sets acquired from multiple instruments. The approach is illustrated and validated through the application to a simulated test case and two real-life industrial metrology scenarios involving structured light scanners and coordinate measurement machines.The results show that not only the proposed approach allows for obtaining final measurement results whose metrological quality transcends that of the original single-sensor datasets, but also it allows to better characterize metrological performance and potential sources of measurement error originated from within each individual sensor.     

全局力平衡PDC钻头布齿优化设计

针对聚晶金刚石复合片(PDC)钻头在钻井破岩过程中因受力不平衡，导致钻井倾斜、井径扩大、钻头产生横向振动和涡动，造成钻头早期失效的问题，基于已有PDC钻头切削力学知识，构建了PDC钻头弯曲力矩及全局力平衡布齿优化设计模型，并提出模型求解方法。布齿设计实例结果表明，采用全局力平衡布齿设计方法得到的布齿结构可使PDC钻头在不同进尺条件下均能保持全局力平衡状态，尤其低进尺（小于1 mm）时的力平衡状态得到极大改善，提高了PDC钻头的钻井稳定性，对于改善钻头受力、提升钻头使用寿命、提高钻井质量与效率具有重要意义。     

An efficient method for estimating time-dependent failure possibility by combining adaptive Kriging with adaptive truncated fuzzy simulation

Time-dependent failure possibility (TDFP) can measure the structural safety level for a time interval of interest under fuzzy uncertainty, but its calculational cost is unaffordable by using fuzzy simulation (FS) due to a required large size of FS candidate sampling pool (CSP). Although time-dependent adaptive Kriging model (T-AK) combined with FS (T-AK-FS) was presented to reduce the number of calling performance function, a large FS CSP still makes training T-AK time-consuming. To improve its efficiency, an adaptive truncated FS (ATFS) with T-AK (T-AK-ATFS) is proposed by CSP size reduction approach. By T-AK-ATFS, the largest safety hypercube in fuzzy standard space is adaptively searched, in which the samples are in safety states and can be removed from the FS CSP. Moreover, T-AK is adaptively trained to search the largest safety hypercube and estimate TDFP simultaneously. In adaptively searching process, the FS CSP is divided into several sub-CSPs, on which training T-AK is more time-saving. Overall, strategies of T-AK-ATFS include proposing ATFS to reduce the FS CSP, adaptively searching the largest safety hypercube, estimating the TDFP with the same T-AK and training T-AK in the sub-CSPs sequentially. Verified by examples, these strategies make T-AK-ATFS more efficient than existing FS and T-AK-FS.     

基于Kriging代理模型的TP304不锈钢裂尖驱动力概率预测（英文）

为了探究材料和载荷的随机性对TP304不锈钢裂尖驱动力的影响规律,通过将弹塑性有限元和Kriging代理模型相结合,实现裂尖驱动力的概率预测。为了提高有限元分析的效率,使用MATLAB对ABAQUS软件的前置处理和后置处理程序进行二次开发,实现随机样本的自动更改、批量计算和概率预测结果的自动分析。研究得到了随机因素作用下TP304不锈钢材料裂尖驱动力的统计分布规律,以及失效概率、失效概率密度函数、累计概率密度函数等概率特征,并对各随机因素的灵敏度进行了分析。最后,通过与Monte Carlo法对比分析了该方法的有效性和效率。结果表明,载荷和材料参数的随机性会显著影响TP304不锈钢裂纹尖端的驱动力,从而影响TP304不锈钢的失效概率,载荷和应变硬化指数对奥氏体TP304不锈钢材料裂尖驱动力的分散性影响最大。     

矿业地质统计学在我国的应用现状及对影响克立格估计的若干因素的研究

地质统计学已用于我国多个矿床的储量计算。变程Ａ是一个很稳健的变异参数，块金Ｃ０、基台Ｃ以及估计邻域的大小较大地影响着克立格估计。     

An emission-weighted proximity model for air pollution exposure assessment

Background

Among the most common spatial models for estimating personal exposure are Traditional Proximity Models (TPMs). Though TPMs are straightforward to configure and interpret, they are prone to extensive errors in exposure estimates and do not provide prospective estimates.

Method

To resolve these inherent problems with TPMs, we introduce here a novel Emission Weighted Proximity Model (EWPM) to improve the TPM, which takes into consideration the emissions from all sources potentially influencing the receptors. EWPM performance was evaluated by comparing the normalized exposure risk values of sulfur dioxide (SO₂) calculated by EWPM with those calculated by TPM and monitored observations over a one-year period in two large Texas counties. In order to investigate whether the limitations of TPM in potential exposure risk prediction without recorded incidence can be overcome, we also introduce a hybrid framework, a ‘Geo-statistical EWPM’. Geo-statistical EWPM is a synthesis of Ordinary Kriging Geo-statistical interpolation and EWPM. The prediction results are presented as two potential exposure risk prediction maps. The performance of these two exposure maps in predicting individual SO₂ exposure risk was validated with 10 virtual cases in prospective exposure scenarios.

Results

Risk values for EWPM were clearly more agreeable with the observed concentrations than those from TPM. Over the entire study area, the mean SO₂ exposure risk from EWPM was higher relative to TPM (1.00 vs. 0.91). The mean bias of the exposure risk values of 10 virtual cases between EWPM and ‘Geo-statistical EWPM’ are much smaller than those between TPM and ‘Geo-statistical TPM’ (5.12 vs. 24.63).

Conclusion

EWPM appears to more accurately portray individual exposure relative to TPM. The ‘Geo-statistical EWPM’ effectively augments the role of the standard proximity model and makes it possible to predict individual risk in future exposure scenarios resulting in adverse health effects from environmental pollution.     

数字地价模型建立过程中的插值方法研究——以上海内环线地区为例

对反距离加权平均(IDW)插值法和克立格(Kriging)插值法两种空间插值方法在数字地价模型(DLPM)建立过程中的插值效果进行比较研究,采用上海内环线地区内的实际样本点数据进行有关实证分析。利用空间插值软件Surfer8.0对上海内环线地区内地价样本点使用上述两种不同的插值方法插值生成不同的插值结果文件,再利用GIS软件MapInfo7.5把插值结果文件生成数字地价栅格专题图,最后用随机选取的验证点对不同的插值结果进行比较分析。研究结果表明:反距离加权平均(IDW)插值法比克立格(Kriging)插值法插值效果更佳,其插值结果更加接近实际地价分布,而且,反距离加权平均(IDW)插值法属于确定性插值法,也简单、易操作,样本点处的插值结果也可以更好的接近样本点的实际地价值,使得数字地价模型(DLPM)的建立更加可靠、可行。     

Mapping of background air pollution at a fine spatial scale across the European Union

Background

There is a need to understand much more about the geographic variation of air pollutants. This requires the ability to extrapolate from monitoring stations to unsampled locations. The aim was to assess methods to develop accurate and high resolution maps of background air pollution across the EU.

Methods

We compared the validity of ordinary kriging, universal kriging and regression mapping in developing EU-wide maps of air pollution on a 1 × 1 km resolution. Predictions were made for the year 2001 for nitrogen dioxide (NO₂), fine particles < 10 µm (PM₁₀), ozone (O₃), sulphur dioxide (SO₂) and carbon monoxide (CO) using routine monitoring data in Airbase. Predictor variables from EU-wide databases were land use, road traffic, population density, meteorology, altitude, topography and distance to sea. Models were developed for the global, rural and urban scale separately. The best method to model concentrations was selected on the basis of predefined performance measures (R², Root Mean Square Error (RMSE)).

Results

For NO₂, PM₁₀ and O₃ universal kriging performed better than regression mapping and ordinary kriging. Validation of the final universal kriging estimates with results from all validation sites gave R²-values and RMSE-values of 0.61 and 6.73 µg/m³ for NO₂; 0.45 and 5.19 µg/m³ for PM₁₀; and 0.70 and 7.69 µg/m³ for O₃. For SO₂ and CO none of the three methods was able to provide a satisfactory prediction.

Conclusion

Reasonable prediction models were developed for NO₂, PM₁₀ and O₃ on an EU-wide scale. Our study illustrates that it is possible to develop detailed maps of background air pollution using EU-wide databases.     

A meshfree model without shear-locking for free vibration analysis of first-order shear deformable plates

Difficulty in imposing essential boundary conditions in the standard element-free Galerkin method (EFG) is due to the lack of Kronecker’s delta function property of shape functions generated by moving least square approximation (MLS). In this paper, we further apply a meshfree model based on the moving Kriging interpolation method (MK) to free vibration analysis of first-order shear deformable plates. The deflection and two rotation field variables of plate are approximated by the MK method, which is employed to construct the shape functions having the delta function property. With this approach, the drawback in enforcement of the boundary conditions caused by the MLS is now avoided. The present formulation is based on the first-order shear deformation plate theory (FSDT) associated with an effective elimination of the shear-locking phenomenon completely, and hence the approach is applicable to both moderately thick and thin plates. Numerical examples considering various aspect ratios and different boundaries are examined and solutions on natural frequencies obtained by the present method are then compared with existing reference solutions, and very good agreements are observed.