Improvement of image characteristics in high-voltage computed tomography (CT) by applying a compressed-sensing (CS)-based image deblurring scheme

We have established a high-voltage computed tomography (CT) system with a 1–3 MV LINAC x-ray generator for industrial nondestructive testing. However, reconstructed CT images are typically blurred, limiting its image performance, mainly due to the finite focal spot size of the x-ray source, aspects inherent to detector such as the detector pixel size and the detector resolution, and the reconstruction procedure as well. Thus the recovery of the reconstructed CT images from their degraded version is essential for improving the image characteristics. In this work, we investigated the compressed-sensing (CS)-based deconvolution scheme for more accurate image deblurring in CT. We implemented the proposed image deblurring algorithm for CT and performed a systematic simulation and experiment to demonstrate its viability. Our results indicate that the proposed deblurring scheme appears to be effective for the blurring problems in CT and is applicable to improve its present image characteristics.     

Evaluating the grain size in curved components using the ultrasonic attenuation method with diffraction correction

Ultrasonic wave propagation in curved components was simulated based on the Multi-Gaussian beam theory to eliminate the negative influence of the surface curvature and water path on grain size attenuation evaluation. The multi-frequency weighted attenuation evaluation model with diffraction correction was introduced into the measured attenuation spectrum to control the systematic error. The experimental results show that for 6 blocks with different curvatures and the same mean grain size, the relative error was reduced from 15.41% to 4.28% by using the presented method, and the standard deviation of the error was 15.92% of that of the traditional method.     

机器视觉技术估算边坡连续形变体积

"自动化"是实现矿山数字化、智能化的关键一步,同时也是矿山生产安全的重要保障.由于矿区环境复杂,造成边坡发生形变产生的原因、形变持续时间存在差异,使得准确提取边坡连续形变信息变得极为困难.为提升该类信息提取的准确度,提出一种基于深度信息的边坡连续形变信息提取方法.首先通过试验模拟矿山边坡,利用深度相机记录边坡在降雨条件...     

基于显著性权重的多曝光融合的单幅雾天图像复原算法

传统暗原色理论的相关算法,在处理雾天图像时会产生颜色的畸变和亮度的损失,针对该情况,该文提出基于权重多曝光融合的单幅雾天图像复原算法。首先通过雾天图像的直方图分析,获取全局大气背景光值的区域。其次构造一种新的Kirsh算子的高阶差分滤波方法,优化透射率图像。最后设计一种基于显著性权重的多曝光图像融合方法,提高处理后图像的视觉效果。该文采用自然图像和合成图像进行实验,与多种算法通过主观和客观评价,表明该文算法比现有的算法有更高的复原效果。     

空间大型末端执行器柔性绳索捕获动力学研究

针对绳索式末端执行器缠绕及捕获卫星过程中的动力学问题,采用绝对节点坐标方法描述横观各向同性柔性绳索的缠绕运动,及卡尔丹角描述刚体卫星的姿态运动,并引入绳索与目标间的非线性动力学模型,建立包含末端执行器固定环和旋转环、绳索及被捕获卫星抓杆在内的刚柔耦合动力学模型。结合广义α法以及工程上常用的Scaling技术,利用Matlab开发了计算程序,实现动力学方程的高效精确数值求解。计算结果表明：此模型能较好地模拟柔性绳索缠绕及捕获卫星的过程,同时给出捕获过程中卫星姿态的变化及绳索的运动和受力情况,为实际工程中捕获机构的设计和试验提供一定的参考依据。     

Diversity in search strategies for ensemble feature selection

Ensembles of learnt models constitute one of the main current directions in machine learning and data mining. Ensembles allow us to achieve higher accuracy, which is often not achievable with single models. It was shown theoretically and experimentally that in order for an ensemble to be effective, it should consist of base classifiers that have diversity in their predictions. One technique, which proved to be effective for constructing an ensemble of diverse base classifiers, is the use of different feature subsets, or so-called ensemble feature selection. Many ensemble feature selection strategies incorporate diversity as an objective in the search for the best collection of feature subsets. A number of ways are known to quantify diversity in ensembles of classifiers, and little research has been done about their appropriateness to ensemble feature selection. In this paper, we compare five measures of diversity with regard to their possible use in ensemble feature selection. We conduct experiments on 21 data sets from the UCI machine learning repository, comparing the ensemble accuracy and other characteristics for the ensembles built with ensemble feature selection based on the considered measures of diversity. We consider four search strategies for ensemble feature selection together with the simple random subspacing: genetic search, hill-climbing, and ensemble forward and backward sequential selection. In the experiments, we show that, in some cases, the ensemble feature selection process can be sensitive to the choice of the diversity measure, and that the question of the superiority of a particular measure depends on the context of the use of diversity and on the data being processed. In many cases and on average, the plain disagreement measure is the best. Genetic search, kappa, and dynamic voting with selection form the best combination of a search strategy, diversity measure and integration method.     

基于遗传算法的立体仓库分拣优化

企业碎片化生产使传统立体仓库的分拣面临难题.针对一种某服装制造企业遇到的立体仓库分拣问题抽象出数学模型,进行简要分析,并将改进的遗传算法应用于该问题.遗传算法子代选择时在轮盘赌方法的基础上引入精英主义原则保证最终收敛结果的稳定性.实验结果表明,遗传算法能快速得到分拣结果.该算法对于立体仓库应用现场具有重要价值.     

The return on investment of the clean coal technology program in the USA

We analyze the return on investment of the U.S. federal government’s clean coal technology (CCT) program for the period 2000–2020. We estimate total costs to government and industry and quantify benefits for: (1) Reduced capital costs of advanced technologies in new plants; (2) Reduced capital and operating costs at existing plants to remain compliant with environmental regulations; (3) Reduced fuel costs due to higher efficiencies; (4) Avoided environmental costs; (5) The value of clean coal technology export sales; (6) Jobs created. We find that benefits over the 20-year period total $111 billion (2008 dollars); the benefits in individual categories range from $15 billion in fuel cost savings to $39 billion for capital and technology cost savings in new and existing plants; and that total jobs created exceed 1.2 million, with an annual average of about 60,000 jobs created. We also find that the return on investment to DOE from the CCT program is favorable and is growing rapidly: By 2020, the cumulative DOE costs will likely total $8.5 billion, for an ROI of more than 13.     

An innovative compressed air energy storage (CAES) using hydrogen energy integrated with geothermal and solar energy technologies: A comprehensive techno-economic analysis - different climate areas- using artificial intelligent (AI)

The present study evaluates the optimal design of a renewable system based on solar and geothermal energy for power generation and cooling based on a solar cycle with thermal energy storage and an electrolyzer to produce hydrogen fuel for the combustion chamber. The subsystems include solar collectors, gas turbines, an electrolyzer, an absorption chiller, and compressed air energy storage. The solar collector surface area, geothermal source temperature, steam turbine input pressure, and evaporator input temperature were found to be major determinants. The economic analysis of the system showed that the solar subsystem, steam Rankine cycle, and compressed air energy storage accounted for the largest portions of the cost rate. The exergy analysis of the system demonstrated that the solar subsystem and SRC had the highest contributions to total exergy destruction. A comparative case study was conducted on Isfahan, Bandar Abbas, Mashhad, Semnan, and Zanjan in Iran to evaluate the performance of the proposed system at different ambient temperatures and irradiance levels during the year. To optimize the system and find the optimal objective functions, the NSGA-II algorithm was employed. The contradictory objective functions of the system included exergy efficiency maximization and cost rate minimization. The optimal Exergy round trip efficiency and cost rate were found to be 29.25% and 714.25 ($/h), respectively.     

结构稀疏通道先验盲图像去模糊方法

针对盲图像去模糊过程中主结构不准确和边缘不清晰问题,提出了一种结构稀疏通道先验(SSCP)盲图像去模糊方法。SSCP表示模糊图像比清晰图像具有更少结构稀疏通道的先验方法。利用SSCP的性能特性,将其作为新的正则化项引入经典去模糊模型,构建盲去模糊新模型,实现对模糊核的准确估计。通过坐标下降法,交替优化求解潜像与模糊核变量。最后,通过反卷积得到去模糊的清晰复原图像,在基准数据集和自然状态模糊图像上开展主观和客观对比实验,并进行人脸和低亮度真实模糊图像的应用拓展实验。实验结果表明,提出的方法在模糊去除、结构恢复、边缘保留和视觉效果方面的性能比经典去模糊方法平均提高了1.72%,通过该方法设计出的计算装置能够实现机器视觉领域中模糊图像的高精度清晰化处理。