机加工孔套管抗挤压强度有限元分析

套管屈服强度直接影响套管抗挤压性能，套管在加工孔过程中可能诱发孔眼附近材料发生金属相变，导致套管屈服强度不再均匀分布，进而影响套管抗挤压强度。在相同工况下，对比了P110完整套管和机加工孔套管的抗挤压强度，以此为参照组分析了相变区域面积和相变区域屈服强度对机加工孔套管抗挤压强度的影响。结果表明，机加工孔会改变材料屈服强度，进而影响套管抗挤压性能。随金属相变区域屈服强度减小，机加工孔套管抗挤压强度减弱；当金属相变区域屈服强度增大时，机加工孔套管抗挤压强度增强。且机加工孔套管相变区域面积对机加工孔套管抗挤压强度的影响随相变区域屈服强度增加而逐渐削弱。     

Technico-economic analysis of off grid solar PV/Fuel cell energy system for residential community in desert region

A technico-economic analysis based on integrated modeling, simulation, and optimization approach is used in this study to design an off grid hybrid solar PV/Fuel Cell power system. The main objective is to optimize the design and develop dispatch control strategies of the standalone hybrid renewable power system to meet the desired electric load of a residential community located in a desert region. The effects of temperature and dust accumulation on the solar PV panels on the design and performance of the hybrid power system in a desert region is investigated. The goal of the proposed off-grid hybrid renewable energy system is to increase the penetration of renewable energy in the energy mix, reduce the greenhouse gas emissions from fossil fuel combustion, and lower the cost of energy from the power systems. Simulation, modeling, optimization and dispatch control strategies were used in this study to determine the performance and the cost of the proposed hybrid renewable power system. The simulation results show that the distributed power generation using solar PV and Fuel Cell energy systems integrated with an electrolyzer for hydrogen production and using cycle charging dispatch control strategy (the fuel cell will operate to meet the AC primary load and the surplus of electrical power is used to run the electrolyzer) offers the best performance. The hybrid power system was designed to meet the energy demand of 4500 kWh/day of the residential community (150 houses). The total power production from the distributed hybrid energy system was 52% from the solar PV, and 48% from the fuel cell. From the total electricity generated from the photovoltaic hydrogen fuel cell hybrid system, 80.70% is used to meet all the AC load of the residential community with negligible unmet AC primary load (0.08%), 14.08% is the input DC power for the electrolyzer for hydrogen production, 3.30% are the losses in the DC/AC inverter, and 1.84% is the excess power (dumped energy). The proposed off-grid hybrid renewable power system has 40.2% renewable fraction, is economically viable with a levelized cost of energy of 145 $/MWh and is environmentally friendly (zero carbon dioxide emissions during the electricity generation from the solar PV and Fuel Cell hybrid power system).     

自适应天牛须优化与K均值聚类的图像分割算法研究

在机器识别中,图像分割是重要的一个步骤,传统分割手段存在一定缺陷。针对传统K均值聚类分割的初始聚类中心敏感的缺陷进行了优化,利用自适应天牛须优化算法,避免了这一问题。通过实验结果表明,该算法(ABASK)对图像进行分割,既可以保证图像轮廓的分割,同时还可以更多地保留图像细节。     

Governance choices and policy outcomes in the Latin American and caribbean electricity sector

The paper first provides, for Latin America and the Caribbean, country-specific synthetic quantitative measures of the degree of adoption of governance reforms in the electricity sector, focusing on four key dimensions (market structure, private sector participation (PSP), regulatory autonomy and operational organization). These measures are then correlated with standard policy performance outcomes measures. The findings suggest that, the reforms, considered jointly, were statistically significantly associated with higher technical and process quality but not with social or service improvements. Considered individually, increased regulatory autonomy enjoyed the highest positive correlation with desirable outcomes, but PSP has no significant correlation with any of the outcomes. In sum, as of 2018, governance reforms, considered jointly, had still not delivered on some promised payoffs, notably in addressing energy poverty, although the sensitivity analysis suggests that, for some indicators, the relationships are stronger for larger than for smaller countries.     

CLOI-NET: Class segmentation of industrial facilities’ point cloud datasets

Shape segmentation from point cloud data is a core step of the digital twinning process for industrial facilities. However, it is also a very labor intensive step, which counteracts the perceived value of the resulting model. The state-of-the-art method for automating cylinder detection can detect cylinders with 62% precision and 70% recall, while other shapes must then be segmented manually and shape segmentation is not achieved. This performance is promising, but it is far from drastically eliminating the manual labor cost. We argue that the use of class segmentation deep learning algorithms has the theoretical potential to perform better in terms of per point accuracy and less manual segmentation time needed. However, such algorithms could not be used so far due to the lack of a pre-trained dataset of laser scanned industrial shapes as well as the lack of appropriate geometric features in order to learn these shapes. In this paper, we tackle both problems in three steps. First, we parse the industrial point cloud through a novel class segmentation solution (CLOI-NET) that consists of an optimized PointNET++ based deep learning network and post-processing algorithms that enforce stronger contextual relationships per point. We then allow the user to choose the optimal manual annotation of a test facility by means of active learning to further improve the results. We achieve the first step by clustering points in meaningful spatial 3D windows based on their location. Then, we apply a class segmentation deep network, and output a probability distribution of all label categories per point and improve the predicted labels by enforcing post-processing rules. We finally optimize the results by finding the optimal amount of data to be used for training experiments. We validate our method on the largest richly annotated dataset of the most important to model industrial shapes (CLOI) and yield 82% average accuracy per point, 95.6% average AUC among all classes and estimated 70% labor hour savings in class segmentation. This proves that it is the first to automatically segment industrial point cloud shapes with no prior knowledge at commercially viable performance and is the foundation for efficient industrial shape modeling in cluttered point clouds.     

浅谈阿里地区蔬菜产业发展现状

阿里地区以蔬菜产业作为主要农业产业,近年来,随着人民生活水平以及对物质生活需求的提升,阿里地区的蔬菜产业也蓬勃发展。文章从阿里地区的基本概况出发,介绍了该地区的农牧业发展现状以及现阶段存在的困难和问题,也给出了下一步发展的建议。以期为阿里地区的蔬菜产业发展提供借鉴。     

Food recognition using neural network classifier and multiple hypotheses image segmentation

ABSTRACT

This paper proposes the multiple-hypotheses image segmentation and feed-forward neural network classifier for food recognition to improve the performance. Initially, the food or meal image is given as input. Then, the segmentation is applied to identify the regions, where a particular food item is located using salient region detection, multi-scale segmentation, and fast rejection. Then, the features of every food item are extracted by the global feature and local feature extraction. After the features are obtained, the classification is performed for each segmented region using a feed-forward neural network model. Finally, the calorie value is computed with the aid of (i) food volume and (ii) calorie and nutrition measure based on mass value. The experimental results and performance evaluation are validated. The outcome of the proposed method attains 0.947 for Macro Average Accuracy (MAA) and 0.959 for Standard Accuracy (SA), which provides better classification performance.     

基于深度语义分割的多源遥感图像海面溢油监测

针对遥感图像海面溢油区域通常受到斑噪声以及强度不均等因素的影响,从而导致溢油区域监测效果较差的问题,本文引入了深度语义分割的方法,将深度卷积神经网络与全连接条件随机场相结合,形成端对端连接。以Resnet结构为基础,首先通过深度卷积神经网络对多源遥感图像粗分割并作为输入,然后经过改进的全连接条件随机场,利用高斯成对势和平均场近似定理,建立条件随机场形成递归神经网络作为输出。通过多源遥感图像对海面溢油区域进行监测,并利用可见光图像估计溢油区域面积。实验在所建立的多源遥感图像数据集上与其它先进模型进行对比,结果表明本文方法提高了溢油区域的分割精度以及精细细节程度,平均交并比为82.1%,监测效果具有明显地改善。     

Microscopic melanoma detection and classification: A framework of pixel‐based fusion and multilevel features reduction

The numbers of diagnosed patients by melanoma are drastic and contribute more deaths annually among young peoples. An approximately 192,310 new cases of skin cancer are diagnosed in 2019, which shows the importance of automated systems for the diagnosis process. Accordingly, this article presents an automated method for skin lesions detection and recognition using pixel‐based seed segmented images fusion and multilevel features reduction. The proposed method involves four key steps: (a) mean‐based function is implemented and fed input to top‐hat and bottom‐hat filters which later fused for contrast stretching, (b) seed region growing and graph‐cut method‐based lesion segmentation and fused both segmented lesions through pixel‐based fusion, (c) multilevel features such as histogram oriented gradient (HOG), speeded up robust features (SURF), and color are extracted and simple concatenation is performed, and (d) finally variance precise entropy‐based features reduction and classification through SVM via cubic kernel function. Two different experiments are performed for the evaluation of this method. The segmentation performance is evaluated on PH2, ISBI2016, and ISIC2017 with an accuracy of 95.86, 94.79, and 94.92%, respectively. The classification performance is evaluated on PH2 and ISBI2016 dataset with an accuracy of 98.20 and 95.42%, respectively. The results of the proposed automated systems are outstanding as compared to the current techniques reported in state of art, which demonstrate the validity of the proposed method.     

The Content of Polyphenolic Compounds in Cocoa Beans (Theobroma cacao L.), Depending on Variety,Growing Region,and Processing Operations: A Review

Polyphenols form the largest group of compounds among natural antioxidants, which largely affect the overall antioxidant and antifree radical activity of cocoa beans. The qualitative and quantitative composition of individual fractions of polyphenolic compounds, even within one species, is very diverse and depends on many factors, mainly on the area of cocoa trees cultivation, bean maturity, climatic conditions during growth, and the harvest season and storage time after harvest. Thermal processing of cocoa beans and cocoa derivative products at relatively high temperatures may in addition to favorable physicochemical, microbiological, and organoleptic changes result in a decrease of polyphenols concentration. Technological processing of cocoa beans negatively affects the content of polyphenolic compounds.