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In order to solve the frosting problem of air source heat pump (ASHP) outdoor heat exchange under low-temperature and low-humidity conditions, a superhydrophobic aluminum (Al) surface with a contact angle (CA) of 158.3° was prepared by chemical etching. The microscopic characteristics of droplet condensation and the freezing process of a superhydrophobic surface were revealed through visual experiments and theoretical analysis. On this basis, the frost-suppression effect of a superhydrophobic Al-based surface simulating the distribution of actual heat exchanger fins was preliminarily explored. The results demonstrated that, due to the large nucleation energy barrier and the coalescence-bounce behavior of droplets, the condensed droplets on the superhydrophobic surface appeared late and their quantity was low. The thermal conductivity of the droplets on a superhydrophobic surface was large, so their freezing rate was low. The frosting amount on the superhydrophobic Al-based surface was 69.79% of that of the bare Al-based surface. In turn, the time required for melting the frost layer on the superhydrophobic Al-based surface was 64% of that on the bare Al-based surface. The results of this study lay an experimental and theoretical foundation for the application of superhydrophobic technology on the scale of heat exchangers.

Counter-roller spinning (CRS), where the mandrel is replaced by rollers, is an effective means of manufacturing large-sized, thin-walled, cylindrical parts with more than 2500 mm diameter. CRS is very complex because of multi-axis rotation, multi-local loading along the circumference, and radial-axial compound deformation. Analytical or experimental methods cannot fully understand CRS. Meanwhile, numerical simulation is an adequate approach to investigate CRS with comprehensive understanding and a low cost. Thus, a finite element (FE) model of CRS was developed with the FORGE code via meshing technology, material modeling, determining the friction condition, and so on. The local fine mesh moving with the roller is one of highlights of the model. The developed 3D-FE model was validated through a CRS experiment by using a tubular blank with a 720 mm outer diameter. The developed 3D-FE model of CRS can provide a basis for parameter optimization, process control, die design, and so on. The data on force and energy predicted by the 3D-FE model can offer reasonable suggestions for determining the main mechanical parameters of CRS machines and selecting the motors. With the predicted data, an all-electric servo-drive system/machine with distributed power was designed in this work for CRS with four pairs of rollers to manufacture a large-sized, thin-walled, cylindrical part with 6000 mm diameter.

A number of spinning methods can be utilized to produce large sheaves such as crosshead sheaves. However, few studies have investigated the relationship and differences between these methods. Therefore, an exploration of spinning features is necessary. In this study, four typical spinning methods, including a novel counter-roller spinning method, were selected to study the forming process. Numerical simulation and experiment study were performed. A theoretical model was then proposed to determine the differences between each spinning method. Stress and strain distribution, thickness reduction, and spinning forces during the forming process were obtained, and results of the simulations and experiments matched well. Variations in the sheave thickness, stress value, and spinning force were observed with different spinning methods. Although all spinning methods investigated in this study could form sheaves, counter-roller spinning with a simple roller method demonstrated the smallest thickness reduction, spinning force, and forming stress. Therefore, the counter-roller spinning should be the first option for spinning large sheaves.

A series of problems brought by the growth of population are perplexing all over the world with the accelerating process of globalization. Therefore, it is urgent to study population prediction, and use the cohort component method and bi-component state space model for predicting population reasonably and effectively, thereby reducing resource waste, and adhering to the path of sustainable development.

The hubness problem widely exists in high-dimensional embedding space and is a fundamental source of error for cross-modal matching tasks. In this work, we study the emergence of hubs in Visual Semantic Embeddings (VSE) with application to text-image matching. We analyze the pros and cons of two widely adopted optimization objectives for training VSE and propose a novel hubness-aware loss function (HAL) that addresses previous methods' defects. Unlike (Faghri et al.2018) which simply takes the hardest sample within a mini-batch, HAL takes all samples into account, using both local and global statistics to scale up the weights of "hubs". We experiment our method with various configurations of model architectures and datasets. The method exhibits exceptionally good robustness and brings consistent improvement on the task of text-image matching across all settings. Specifically, under the same model architectures as (Faghri et al. 2018) and (Lee at al. 2018), by switching only the learning objective, we report a maximum R@1improvement of 7.4% on MS-COCO and 8.3% on Flickr30k., AAAI-20 (to appear)

The titanium alloy strut is a key load-bearing part for landing gear in aircraft. Closed-die forging with flash is adopted to manufacture the titanium alloy strut. The initial position of preform also has a notable influence on the cavity fill and metal lost into flash in the forging process. Complete die filling and even metal lost into flash at different portions of forging are the best forging process. Thus, an optimization method of initial position of preform for large-scale strut forging was developed based on the cavity fill and flash size. According to the geometrical characteristics and the deformation characteristics, a design variable x (description of initial position) and the fill indexes η 1 and η 2 were selected and defined. The objective function y consists of fill indexes. Numerical simulation combining algorithm of dichotomy to generate sampling data, and then the objective function can be produced. The finite element analysis was carried out for the optimum initial position searched by the objective function. If the simulation results is in the error range (such as

The effective and automated parsing of building facades from terrestrial laser scanning (TLS) point clouds of urban environments is an important research topic in the GIS and remote sensing fields. It is also challenging because of the complexity and great variety of the available 3D building facade layouts as well as the noise and data missing of the input TLS point clouds. In this paper, we introduce a novel methodology for the accurate and computationally efficient parsing of urban building facades from TLS point clouds. The main novelty of the proposed methodology is that it is a systematic and hierarchical approach that considers, in an adaptive way, the semantic and underlying structures of the urban facades for segmentation and subsequent accurate modeling. Firstly, the available input point cloud is decomposed into depth planes based on a data-driven method; such layer decomposition enables similarity detection in each depth plane layer. Secondly, the labeling of the facade elements is performed using the SVM classifier in combination with our proposed BieS-ScSPM algorithm. The labeling outcome is then augmented with weak architectural knowledge. Thirdly, least-squares fitted normalized gray accumulative curves are applied to detect regular structures, and a binarization dilation extraction algorithm is used to partition facade elements. A dynamic line-by-line division is further applied to extract the boundaries of the elements. The 3D geometrical facade models are then reconstructed by optimizing facade elements across depth plane layers. We have evaluated the performance of the proposed method using several TLS facade datasets. Qualitative and quantitative performance comparisons with several other state-of-the-art methods dealing with the same facade parsing problem have demonstrated its superiority in performance and its effectiveness in improving segmentation accuracy.

Large sheaves, such as crosshead sheaves, are foundation parts in the heavy industry. Counter-roller spinning steel sheaves are utilized to replace traditional casting iron parts. Few studies exist on the groove shape of these spinning sheaves. Consequently, it is significant to explore the spinning groove shape variation rule and confirm the appropriate spinning parameters. Both experimental and numerical methods were utilized to study the groove shape variation of Q235 steel sheaves and their results were well matched. Spring-back phenomena were considered in this study. The groove depth was lower than the spinning depth and the last formed groove was the deepest. The former groove depth would be affected by the adjacent following spinning process. The single groove spinning result was linearly dependent on the multiple spinning groove depth. Certain equations were used to calculate the groove depth. The bottom&ndash, middle&ndash, top spinning sequence, which was better than other spinning sequences, should be used in the sheave spinning method. A sheave spinning process could be designed based on the study to obtain a fine groove shape.

The Industry 4.0 is regard as the fourth industrial revolution which is geared to the demand of customization products. As one kind of basic components in industry, the small complicated axisymmetric parts are usually low-volume customization products. So it is significant to explore their manufacturing process in the Industry 4.0 era. The production procedure of a representative 4 stages cone has been studied. First, requirements of customers are sent to the industrial internet. Then technology suppliers will design an appropriate forming process with the CAD (Computer-Aided Design), CAE (Computer-Aided Engineering), CAPP (Computer-Aided Process Planning), CAM (Computer-Aided Manufacturing) and other Industry 4.0 technologies. Finally, distributed smart factories will organize production and send target products to customers depending on the industrial internet. Based on this study, it can be seen that the metal spinning method is suitable for the small complicated axisymmetric parts producing in Industry 4.0. The remote customization manufacturing will be more popularize. Even, the CAD, CAE, CAPP and CAM method will play more important roles in manufacture with the help of industrial internet.

Semantic parsing of large-scale 3D point clouds is an important research topic in computer vision and remote sensing fields. Most existing approaches utilize hand-crafted features for each modality independently and combine them in a heuristic manner. They often fail to consider the consistency and complementary information among features adequately, which makes them difficult to capture high-level semantic structures. The features learned by most of the current deep learning methods can obtain high-quality image classification results. However, these methods are hard to be applied to recognize 3D point clouds due to unorganized distribution and various point density of data. In this paper, we propose a 3DCNN-DQN-RNN method which fuses the 3D convolutional neural network (CNN), Deep Q-Network (DQN) and Residual recurrent neural network (RNN) for an efficient semantic parsing of large-scale 3D point clouds. In our method, an eye window under control of the 3D CNN and DQN can localize and segment the points of the object class efficiently. The 3D CNN and Residual RNN further extract robust and discriminative features of the points in the eye window, and thus greatly enhance the parsing accuracy of large-scale point clouds. Our method provides an automatic process that maps the raw data to the classification results. It also integrates object localization, segmentation and classification into one framework. Experimental results demonstrate that the proposed method outperforms the state-of-the-art point cloud classification methods., IEEE International Conference on Computer Vision (ICCV) 2017

In recent years, rapid development E-commerce has occurred in China. E-commerce and longtail economy are playing an increasingly important role in the national economy. How to accurately forecast the sales of E-commerce website is valuable. On the basis of building theoretical frame of keywords search queries and online shopping transaction volume, this paper took Taobao.com - the largest E-commerce retail website in China-as an example, composed the keywords into composite index by stepwise synthesis, then analyzed the relationship between the sales of different categories of goods and the corresponding composite index through the regression model, made a prediction test, and finally got the forecast results of daily sales for different categories goods. Compared the forecast results to the actual data, the Mean Absolute Percentage Error is fewer than nine percent.

The spherical joint clearance is a key error factor, which influenced and restricted the application of parallel mechanism in high precision field. This paper discusses the regularity of the spherical joint clearance in the parallel mechanism and its influence on the accuracy of the parallel mechanism in both theoretical and experimental aspects. A spherical joint clearance measuring instrument is introduced and used to measure the joint clearance. And the relationship between the clearance and its work pose is revealed. Based on the theoretical and experimental analysis, it is concluded that the clearance of the spherical joint is near-linear proportional to the applied load as well as the clearances in different poses obey the Rayleigh distribution approximately under the same load.

A new 3-PUU parallel CMM is proposed in this paper, and according to kinematic model space coordinate of measuring head is solved with the readings of three grating reading heads. Measuring accuracies affected by errors of parallel structure parameters are mainly analyzed and measuring error model is established. The error analysis interface of measuring head coordinate and data processing results are finished by using Matlab, and based on the results of key errors analysis 3-PUU parallel CMM is further optimized by improving accuracy of manufacturing and assembling workpiece, calibration and error compensation of structure parameters.

An analytical method was developed for the determination of cefalonium in milk by high performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS). A total of 1 g milk was deproteinized by acetonitrile. The supernatant was transferred into a test tube to be blown dry with N2 at 37 degrees C. Then the residue was dissolved with methanol-0.1% formic acid in water (3:7, v/v). The sample was determined by HPLC-MS/MS after the purification. The chromatographic separation was achieved on a C18 column using 0.1% formic acid in water and acetonitrile as mobile phases with gradient elution. Qualitative and quantitative analyses were achieved by HPLC-MS/MS under positive ionization and multiple reaction monitoring (MRM) mode. Matrix-matched calibration curve was used for the quantification. Good correlation coefficients were obtained (r > 0. 999) in the mass concentration range of 2-200 microg/L. The limit of detection (LOD, S/N > or = 3) was 0.5 microg/kg in milk, and the limit of quantification (LOQ, S/N 10) was 2 microg/kg. The mean recoveries at the four levels of LOQ, 1/2MRL (maximum residue level), MRL, 2MRL were between 78.5% and 86.2%, with the intra-day relative standard deviations (RSDs) of 1.5% to 6.2% and inter-day RSDs of 2.9% to 5.6%. In conclusion, the established method can be applied for the determination of cefalonium residue in milk.