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。     

Sparse decomposition method based on time–frequency spectrum segmentation for fault signals in rotating machinery

The impulse signal in large rotating machinery with damage fault is sparse, weak, coupled, and even nonperiodic in intermittent operation. To extract this complex signal is a key topic in machinery fault diagnosis. Sparse decomposition (SD) has excellent adaptability in describing arbitrary complex signals based on over-complete dictionary. However, the pursuit speed of best atom is a serious drawback. To alleviate this, a method of sparse decomposition based on time–frequency spectrum segmentation (SD-TFSS) is introduced. Generalized S transform (GST) provides the capability to show the distribution of vibration signals, but the resolution is susceptible to noise, multiresolution generalized S-transform (MGST) is developed to generate multiresolution time–frequency spectrums. Then, spectrums fusion with an appropriate threshold is adopted to acquire multiresolution binary spectrums and produce an optimal binary spectrum. From this optimal binary spectrum, all the connectivity areas are extracted and marked by spectrum segmentation. Thus, an optimal library can be constructed by selecting the optimal atoms of every connectivity area, and the signal can be expressed with this library. We conduct simulations and experiments demonstrating that the proposed method performs well with lower pursuit complexity, higher decomposition efficiency, and better approximation precision.     

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.     

文旅融合下的京津冀校园文化衍生品设计研究

探析如何让京津冀地区独特的校园文化衍生品得到创新性发展，让文创产品更容易达到共情的目的，并传递不同的校园文化价值，围绕校园文化衍生品进行创新性设计。基于对京津冀地区不同校园文化背景的调研，在文旅融合背景下分析校园文化产品的现状与发展，以及有代表性的校园文化品牌。以清华大学文创产品为例，设计出具有创新性的校园文化产品，将文化元素与产品设计相结合。运用校园特色建筑、色彩、文化背景，发挥其原创性、传播性、文化性等优势，传播学校的理念及文化，扩大校园文化传播途径。在文旅融合背景下，设计出独特的校园文化衍生产品，传承文化内涵与理念，并提高校园文化认同感与归属感，成为京津冀地区发展校园文化的趋势，从而推动文化旅游产业的发展。     

A star pattern recognition algorithm for cameras with large FOV

In most large field of view (FOV) observations, the distortion problem is inevitably and significantly more serious than in small FOV ones. In the circumstances, many traditional star identification approaches are not able to efficiently identify stars any more. In order to deal with this problem, we put forward a star identification method that is less sensitive to distortion. The method first processes stars in the central area of the image, using traditional identification logic, and then applies the region growing strategy to enlarge the identified regions iteratively until the entire image is covered. The performance of the new scheme is analysed in the presence of both simulated data and real data. The results show that the proposed algorithm has the advantage of speed, and the strategy of regional extension can efficiently identify stars in large FOV images compared with other existing algorithms.     

Babesia Bovis Ligand-Receptor Interaction: AMA-1 Contains Small Regions Governing Bovine Erythrocyte Binding

Apical membrane antigen 1 is a microneme protein which plays an indispensable role during Apicomplexa parasite invasion. The detailed mechanism of AMA-1 molecular interaction with its receptor on bovine erythrocytes has not been completely defined in Babesia bovis. This study was focused on identifying the minimum B. bovis AMA-1-derived regions governing specific and high-affinity binding to its target cells. Different approaches were used for detecting ama-1 locus genetic variability and natural selection signatures. The binding properties of twelve highly conserved 20-residue-long peptides were evaluated using a sensitive and specific binding assay based on radio-iodination. B. bovis AMA-1 ectodomain structure was modelled and refined using molecular modelling software. NetMHCIIpan software was used for calculating B- and T-cell epitopes. The B. bovis ama-1 gene had regions under functional constraint, having the highest negative selective pressure intensity in the Domain I encoding region. Interestingly, B. bovis AMA-1-DI (¹⁰⁰YMQKFDIPRNHGSGIYVDLG¹¹⁹ and ¹²⁰GYESVGSKSYRMPVGKCPVV¹³⁹) and DII (³⁰²CPMHPVRDAIFGKWSGGSCV³²¹)-derived peptides had high specificity interaction with erythrocytes and bound to a chymotrypsin and neuraminidase-treatment sensitive receptor. DI-derived peptides appear to be exposed on the protein’s surface and contain predicted B- and T-cell epitopes. These findings provide data (for the first-time) concerning B. bovis AMA-1 functional subunits which are important for establishing receptor-ligand interactions which could be used in synthetic vaccine development.     

Heat transfer enhancement in oscillatory two-phase flow of Casson and ferrofluid comprising differently shaped nanoparticles in a horizontal composite channel

The most commonly discussed topic at the present time is the fluid flow in a channel having a porous area, as it is of practical importance for petroleum extraction, frequently isolated irrigation, coolant circulation, biofluid transportation in living organisms, and industrial cleaning systems. An investigation of heat transfer characteristics of unsteady magnetohydrodynamics oscillatory two-immiscible fluid flow of Casson fluid (CF) and ferrofluid (FF) in a long-infinite horizontal composite channel is performed analytically. The channel is divided into two regions. Region I is occupied by a porous region with CF, while Region II is a clear region filled with FF. The mathematical system of coupled partial differential equations is solved analytically considering the two-term periodic and nonperiodic functions. The influences of physical parameters such as CF parameter, porosity parameter, nanoparticles volume fraction, Hartmann number, periodic frequency parameter, oscillations amplitude, and pressure on momentum as well as heat transfer are presented through graphical illustrations (two-dimensional along with three-dimensional) and in tabular form using the MATHEMATICA program. Four different shaped nano-size ferroparticles are used in this study. The investigation of four different nanosized ferroparticles exhibits that the momentum transfer is higher when brick-shaped nanosized ferroparticles are added to the base fluid, water. It is also observed that thermal performance enhances in the case of brick-shaped nanosized ferroparticles compared to the blade, cylinder, and platelet-shaped nanosized ferroparticles. It is observed that the dispersion of brick-shaped nanosized ferroparticles is recommended in base fluid water for greater thermal performance through a horizontal channel.     

滚球半径法确定瓦斯抽放泵站放空管合理安置区域

为了确定瓦斯抽放泵站放空管合理安置区域。在滚球半径法推算原理的基础上,利用几何知识,使用AutoCAD绘图软件绘制瓦斯抽放泵站放空管合理安置区域,而非传统方法的若干公式计算。AutoCAD绘图软件作图的方法避免了《GB 50057-2010建筑物防雷设计规范》附录D中传统方法繁琐的计算。确定瓦斯抽放泵站放空管合理安置区域作图法直观、简单、清晰、明了,简化了瓦斯抽放泵站防雷设计工作。为科学合理确定瓦斯抽放泵站放空管位置提供了依据。     

Display illumination with modulated directional backlight

Illumination is essential for modern life as colorful world is perceived by human visionary system. Display technology has been developing rapidly in recent decades, and the basic principle is related to the way that the image is illuminated and light is emanated. Traditional illumination is provided by different types of light sources, and the display image is visible in large viewing space until the emanating light decays to zero. This work proposes and demonstrates a novel illumination scheme for a display in which the displaying images are visible only in specific spatial regions. The directional backlight ensures the image propagating to specific direction while imaging visibility can be controlled to terminate abruptly at certain distance from the display screen while exerting no influence to nearby regions. The working principle for such an illumination scheme is the use of the modulated coherent directional backlight through an axicon lens. It is shown that the illumination scheme can robustly deliver carried image information to the designated viewing region. This new illumination scheme has many advantages over conventional illumination, including its usage for personal display, very lower energy consumption, as well as minimizing light hazard pollution.     

A Feature Selection Strategy to Optimize Retinal Vasculature Segmentation

Diabetic retinopathy (DR) is a complication of diabetes mellitus that appears in the retina. Clinitians use retina images to detect DR pathological signs related to the occlusion of tiny blood vessels. Such occlusion brings a degenerative cycle between the breaking off and the new generation of thinner and weaker blood vessels. This research aims to develop a suitable retinal vasculature segmentation method for improving retinal screening procedures by means of computer-aided diagnosis systems. The blood vessel segmentation methodology relies on an effective feature selection based on Sequential Forward Selection, using the error rate of a decision tree classifier in the evaluation function. Subsequently, the classification process is performed by three alternative approaches: artificial neural networks, decision trees and support vector machines. The proposed methodology is validated on three publicly accessible datasets and a private one provided by Hospital Sant Joan of Reus. In all cases we obtain an average accuracy above 96% with a sensitivity of 72% in the blood vessel segmentation process. Compared with the state-of-the-art, our approach achieves the same performance as other methods that need more computational power. Our method significantly reduces the number of features used in the segmentation process from 20 to 5 dimensions. The implementation of the three classifiers confirmed that the five selected features have a good effectiveness, independently of the classification algorithm.