餐饮废油生物柴油低温流动性的改进研究

使用气-质联用仪测定餐饮废油生物柴油(WCME)和-10号柴油(-10PD)的组成,使用冷滤点试验器和运动黏度试验器测定WCME的低温流动性,同时使用调合、添加低温流动性改进剂的方法改进WCME的低温流动性。实验结果表明,WCME主要由饱和脂肪酸甲酯和不饱和脂肪酸甲酯组成,质量分数分别为27.63%和71.81%;WCME冷滤点为0℃,运动黏度(40℃)为4.41mm2/s;WCME与-10PD调合后,冷滤点降低,其中B20的冷滤点最低,为-13℃,运动黏度随着WCME的体积分数的减少,逐渐接近-10PD的运动黏度。通过添加低温流动性改进剂,WCME,B10,B20的冷滤点分别从0,-8,-13℃降至-4,-26,-25℃。     

GOS+MU:一种查询对象选择新方法

在分析单一MU(Most Uncertainty)采样缺陷的基础上,提出一种"全局最优搜寻"方法 GOS(Global Optimal Search),并结合MU共同完成查询选择。GOS+MU方法中,GOS着眼全局寻找目标,在应用环境能提供的训练样本数量有限、分类器受训不充分时,该方法选择的对象学习价值高,能快速推进分类器学习进程;MU则能够在GOS采样失效情形下,利用分类器当前训练成果,选择查询不确定性最强的样本补充训练集。通过对网络商品的用户评论进行分类仿真,并比较其他采样学习方法的效果,证明了GOS+MU方法在压缩学习成本、提高训练效率方面的有效性。     

Prediction of methane adsorption content in continental coal-bearing shale reservoir using SLD model

Shale gas, as an important unconventional resource, has drawn global attention. It is mainly composed of adsorption gas and free gas. Adsorption gas content could play an important guiding role on both the selection of favorable perspective area and the exploration and exploitation of shale gas resources. In order to accurately measure adsorption gas content, a new approach was established to predict the adsorption isotherm of methane on shale. Based on the simplified local-density (SLD) method, both the adsorption isotherms of illite, illite/smectite mixed-layer, cholorite and type III kerogen and the total shale rock could be well fitted. The fitting results show good coincidences with the true experimental test data, which proves the method is reasonable and dependable and the prediction results are effective and credible. In addition, the good simulation results show that the SLD parameters can reflect the pore structure characteristics and corresponding adsorption characteristics of the shale samples, which can be used for the quantitative characterization of shale pore system.     

A lightweight autonomous MAV for indoor search and rescue

Micro Aerial Vehicles (MAVs) have great potentials to be applied for indoor search and rescue missions. In this paper, we propose a modular lightweight design of an autonomous MAV with integrated hardware and software. The MAV is equipped with the 2D laser scanner, camera, mission computer and flight controller, running all the computation onboard in real time. The onboard perception system includes a laser‐based SLAM module and a custom‐designed visual detection module. A dual Kalman filter design provides robust state estimation by multiple sensor fusion. Specifically, the fusion module provides robust altitude measurement in the circumstance of surface changing. In addition, indoor‐outdoor transition is explicitly handled by the fusion module. In order to efficiently navigate through obstacles and adapt to multiple tasks, a task tree‐based mission planning method is seamlessly integrated with path planning and control modules. The MAV is capable of searching and rescuing victims from unknown indoor environments effectively. It was validated by our award‐winning performance at the 2017 International Micro Air Vehicle Competition (IMAV 2017), held in Toulouse, France. The performance video is available on https://youtu.be/8H19ppS_VXM .     

Stream-Based Data Sampling Mechanism for Process Object

Process object is the instance of process. Vertexes and edges are in the graph of process object. There are different types of the object itself and the associations between object. For the large-scale data, there are many changes reflected. Recently, how to find appropriate real-time data for process object becomes a hot research topic. Data sampling is a kind of finding c hanges o f p rocess o bjects. There i s r equirements f or s ampling to be adaptive to underlying distribution of data stream. In this paper, we have proposed a adaptive data sampling mechanism to find a ppropriate d ata t o m odeling. F irst o f all, we use concept drift to make the partition of the life cycle of process object. Then, entity community detection is proposed to find changes. Finally, we propose stream-based real-time optimization of data sampling. Contributions of this paper are concept drift, community detection, and stream-based real-time computing. Experiments show the effectiveness and feasibility of our proposed adaptive data sampling mechanism for process object.     

Ni-Foam Structured Ni-Phyllosilicate Ensemble as an Efficient Monolithic Catalyst for CO2 Methanation

This work proposed a new path to synthesize Ni-phyllosilicate through the reaction of nickel hydroxide and silica sol on the surface of Ni-foam to form the monolithic Ni-phyllosilicate/Ni-foam catalyst. Ni-phyllosilicate could reprint the morphology of nickel hydroxid and firmly anchor on the framework of Ni-foam, which obtained fine Ni particles of 2.8 nm after reduction in H₂ at 650 °C, resulting in high catalytic activity for CO₂ methanation. In addition, the Ni-phyllosilicate/Ni-foam catalyst showed high long-term stability in a 100 h-lifetime test owing to the combined effects of surface confinement of Ni-phyllosilicate, firm anchoring between Ni-phyllosilicate and Ni-foam, as well as the high heat transfer property of Ni-foam.

Graphical Abstract

     

Citation recommendation based on citation tendency

Scientometrics - Due to the development of academic, more and more attentions are paid to citation recommendation. To solve the citation recommendation problem, researchers begin to focus on the...     

Hierarchical Attention for Part-Aware Face Detection

International Journal of Computer Vision - Expressive representations for characterizing face appearances are essential for accurate face detection. Due to different poses, scales, illumination,...