Road Extraction from High Resolution Remote Sensing Image based on Shape Analysis with Gestalt Theory

Simulating the psychological experience of human vision，a road extraction model based on the format tower is proposed to extract the road in the high resolution remote sensing image from the perspective of morphology.Firstly，based on the spectral and texture information，the suspected road targets are extracted by using segmentation technology.Then these targets are classified according to their reliability and extract the road targets for each category.Finally，three types of identified road information are verified and merged，and the continuous smooth road extraction results are obtained.Experiments on real high resolution images show that the results are consistent with the visual perception of the human eye，and the overall classification accuracy is higher，indicating that the algorithm is effective and feasible and has good use value.     

Tuning effect of silicon substitution on magnetic and high frequency electromagnetic properties of R2Fe17 and their composites

In this work, we report the tuning effect of the Si substitution on the magnetic and high frequency electromagnetic properties of R₂Fe₁₇ compounds and their paraffin composites. It is found that the introduction of Si can remarkably improve the magnetic and electromagnetic properties of the R₂Fe₁₇ compounds, making the R₂Fe_17–xSi_x-paraffin composites excellent microwave absorption materials (MAMs). By introducing the Si element, their saturation magnetizations decrease slightly, while much higher Curie temperatures are obtained. Furthermore, better impedance match is reached due to the decrease of the high-frequency permittivity ε′ by about 40%–50%, which finally enhances the performance of the microwave absorption. The peak frequency (f_RL) of the reflection loss (RL) curve moves toward high frequency domain and the qualified bandwidth (QB, RL ≤ ?10 dB) increases remarkably. The maximum QB of 3.3 GHz (12.0–15.3 GHz) is obtained for the Sm_1.5Y_0.5Fe₁₅Si₂-paraffin composite (d = 1.0 mm) and the maximum RL of ?53.6 dB is achieved for Nd₂Fe₁₅Si₂-paraffin composite (d = 2.2 mm), both surpassing most of the reported MAMs. Additionally, a distinguished dielectric microwave absorption peak is observed, which further increases the QB in these composites.     

碎软煤层条带瓦斯地面高效抽采技术研究

为了提高碎软煤层条带瓦斯抽采效率和效果,基于目前地面瓦斯抽采主要采用垂直井或从式井的方式抽采效果差、效率低的现状,通过理论和实验分析论证了穿岩层压裂改造煤储层的可行性,提出了在目标煤层顶板岩层中钻水平井,并通过垂直向下射孔以及采用泵送桥塞分段进行压裂的方式进行地面瓦斯抽采。试验结果表明:顶板分段压裂水平井单井产量高、高稳产期更长、产量衰减更慢;有效水平井段控制区域内瓦斯下降均匀,更有利于进行条带瓦斯抽采;相同投资条件下,采用水平井的方式瓦斯抽采效率和投入产出比更高。     

Liquid phase sintering and characterization of SiC ceramics

The present study focuses on the sintering of silicon carbide-based ceramics (SiC) by liquid phase sintering (LPS) followed by characterization of the produced ceramics. AlN/Re₂O₃ mixtures were used as additives in the LPS process. In the first step, the LPS-SiC materials were produced in a graphite resistance furnace in the form of discs at different temperatures. The conditions with the best results regarding real density and relative density were taken as reference for sintering in the form of prismatic bars. In the second step, these samples were evaluated regarding fracture toughness (K_IC), by the Single Edge V Notch Beam – SEVNB – method, and flexural strength. K_IC behavior was evaluated according to the depth and curvature radius of the notches. Reliable K_IC values were presented when the ceramic displayed a small curvature radius at the notch tip. When the radius was large, it did not maintain the square root singularity of the notch tip. Tests were carried out to determine K_IC values in atmospheric air and water. K_IC results were lower in water than air, with a decrease ranging between 2.56% and 11.26%. The observations indicated a direct grain size correlation between K_IC values and fracture strength of the SiC ceramics.     

Formation and properties of high entropy oxides in Co-Cr-Fe-Mg-Mn-Ni-O system: Novel (Cr,Fe,Mg,Mn,Ni)3O4 and (Co,Cr,Fe,Mg,Mn)3O4 high entropy spinels

Possibility of formation of quinary and senary equimolar high entropy oxides from the Co-Cr-Fe-Mg-Mn-Ni-O system is presented. Different proposed compositions are synthesized using the solid-state reaction route at high temperatures (900−1100 °C) and quenched to room temperature. Phase composition of the samples is studied, showing tendency toward formation of two main phases: rock salt-structured Fm-3 m and spinel-structured Fd-3 m. It is documented that the annealing temperature has a profound effect on stability of both structures, and at 1100 °C usually the highest content of Fm-3 m phase is usually observed. Three different oxides, namely, (Co,Cr,Fe,Mn,Ni)₃O₄, (Co,Cr,Fe,Mg,Mn)₃O₄ and (Cr,Fe,Mg,Mn,Ni)₃O₄ are obtained as single-phase materials, which structure can be described as the high entropy Fd-3 m spinel one. The latter two compounds have not been previously reported in the literature. Activated character of the electrical conductivity dependence on temperature is observed, with relatively high total conductivity at high temperatures and corresponding high absolute values of Seebeck coefficient.     

因素空间理论下基点分类算法研究

目前，基于因素空间理论的背景基提取算法计算过程复杂，初始化必须依赖各因素极值，基点数量提取冗余等原因，未能在应用中取得很好效果。为此，结合内点判别法和知识可继承、可扩展的思想，提出一种计算简单、初始化独立、基点数量小的改进的背景基提取算法。然后，利用改进的背景基提取算法构造出一种全新的数据分类算法-基点分类算法，基点分类算法以提取每一类样本的背景基为预测模型，再通过新定义的λ-背景基，优化预测模型。数值实验表明:基点分类算法原理简单、构造难度小、分类模型泛化能力强，预测能力准确率高，同时严格的模型限定区域又能为识别新类别提供新方法。     

单相硼化物的制备及摩擦磨损性能研究

为了深入研究渗硼层中硼化物的性能，采用真空感应熔炼法制备单相硼化物材料。观察分析制备的硼化物微观组织，测试其力学性能。采用MMU-5G型销-盘式摩擦磨损试验机，在干摩擦条件下，研究了不同载荷下单相硼化物的摩擦学性能，观察其磨损表面形貌特征，探讨其磨损方式。结果表明：制备的硼化物为单一相，试样纯度高，试样的平均显微硬度为HV2065，平均断裂韧性值为1.68 MPa·m1/2；硼化物的断口处没有宏观的塑性变形，断口齐平光亮，表现为脆性断裂特征；干摩擦条件下随着载荷从10 N增加到30 N，硼化物的摩擦因数先降低后增加，20 N载荷时达到最小值，而其磨损量随着载荷的增加不断上升；随着载荷从10 N增加到30 N，磨损表面的粗糙度先逐渐上升后急剧上升； 10~20 N载荷下，硼化物的磨损以磨粒磨损为主，而25~30 N载荷下，硼化物的主要磨损方式从磨粒磨损转变为脆性破损。     

One-step synthesis of high purity ZnO micro/nanostructures from pure Zn and pre-alloyed brass powders by vapor phase transport

In this research, vapor phase transport (VPT) was introduced as a facile, inexpensive method to produce ZnO micro/nanostructures from various Zn sources such as pure Zn and alpha brass pre-alloyed powders (Cu–20Zn and Cu–28Zn) at different processing temperatures of 930 °C–1050 °C. Simultaneous thermal analysis (STA) was carried out to investigate Zn evaporation and ZnO micro/nanostructure formation. STA results showed an exothermic peck at 711 °C and 728 °C for Cu–20Zn and Cu–28Zn, respectively, due to oxidation of the evaporated Zn element and formation of ZnO micro/nanostructures. X-ray diffraction results showed that high purity ZnO micro/nanostructures were successfully synthesized via VPT process and the crystallite size was increased from ~60 nm to ~100 nm with increasing processing temperature. Field emission scanning electron microscopy observations showed morphology (e.g. rods, column, tetrapods, and combs) and size of the synthesized micro/nanostructures were dependent on the Zn sources and processing temperature, in which average diameter of the synthesized ZnO structures was increased with increasing the processing temperature. The smallest (98 nm) and largest (603 nm) average diameters of synthesized ZnO micro/nanostructures were attained from the pure Zn and Cu–28Zn brass powders at 930 °C and 1050 °C, respectively.     

萃取塔数学模型及过程强化的若干研究进展

萃取塔因生产能力大、占地面积小、密闭性好等优点，在石油、化工、生物、医药和环境工程等多领域被广泛应用。本文从以下几个方面介绍了萃取塔近些年的研究进展：综述了传统萃取塔（脉冲萃取塔、转盘塔与Kühni塔等）的水力学、轴向扩散与传质模型的发展，分析比较了表面张力、传质方向、放大效应等因素对模型的影响；介绍了计算流体动力学（CFD）在萃取塔中单液滴、单相流模拟、液-液两相流模拟、外加能量模拟、与群体平衡模型（PBM）耦合模拟中的应用进展；介绍了国内外设计开发的新型萃取塔，包括改变传统塔的内构件和引入多种外场能量等方式来强化相间传质。研究表明，将先进实验研究方法、准确经验模型和可靠理论计算相结合，将会是萃取塔研究的重要手段和方向。     

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.