Study on Recognition Method of Similar Weather Scenes in Terminal Area

Weather is a key factor affecting the control of air traffic. Accurate recognition and classification of similar weather scenes in the terminal area is helpful for rapid decision-making in air traffic flow management. Current researches mostly use traditional machine learning methods to extract features of weather scenes, and clustering algorithms to divide similar scenes. Inspired by the excellent performance of deep learning in image recognition, this paper proposes a terminal area similar weather scene classification method based on improved deep convolution embedded clustering (IDCEC), which uses the combination of the encoding layer and the decoding layer to reduce the dimensionality of the weather image, retaining useful information to the greatest extent, and then uses the combination of the pre-trained encoding layer and the clustering layer to train the clustering model of the similar scenes in the terminal area. Finally, terminal area of Guangzhou Airport is selected as the research object, the method proposed in this article is used to classify historical weather data in similar scenes, and the performance is compared with other state-of-the-art methods. The experimental results show that the proposed IDCEC method can identify similar scenes more accurately based on the spatial distribution characteristics and severity of weather; at the same time, compared with the actual flight volume in the Guangzhou terminal area, IDCEC's recognition results of similar weather scenes are consistent with the recognition of experts in the field.     

SVPWM教学研究之重点解析

SVPWM是电力电子技术教学中的重要内容,本文对空间矢量调制的教学重点做了一个较为全面系统的梳理。重点阐述SVPWM的核心思想,特别是三相正弦交流电SVPWM的基本原理,严格证明了三相电压的空间矢量相等与线电压相等等价。同时,证明在SVPWM下,逆变器输出的最大圆为基本空间矢量构成的正六边形的内接圆、直流母线电压利用率提高了约15.4%。     

Editorial: Machine Learning and Intelligent Communications (MLICOM 2018)

Mobile Networks and Applications -     

Confidence regions of two-stage stochastic linear complementarity problems

Two-stage stochastic linear complementarity problems (TSLCP) model a large class of equilibrium problems subject to data uncertainty, and are closely related to two-stage stochastic optimization problems. The sample average approximation (SAA) method is one of the basic approaches for solving TSLCP and the consistency of the SAA solutions has been well studied. This paper focuses on building confidence regions of the solution to TSLCP when SAA is implemented. We first establish the error-bound condition of TSLCP and then build the asymptotic and nonasymptotic confidence regions of the solutions to TSLCP by error-bound approach, which is to combine the error-bound condition with central limit theory, empirical likelihood theory, and large deviation theory.     

A novel approach of data race detection based on CNN-BiLSTM hybrid neural network

Neural Computing and Applications - Existing data race detection approaches based on deep learning are suffering from the problems of unique feature extraction and low accuracy. To this end, this...     

Heterogeneous feature ensemble modeling with stochastic configuration networks for predicting furnace temperature of a municipal solid waste incineration process

Neural Computing and Applications - Considering the accuracy, generalization ability, stability, and training efficiency of a furnace temperature model in the process of municipal solid waste...     

机械结构相似度评价及可靠性预计研究

在用传统的相似产品法进行可靠性预计时,所选取的相似产品与评价对象之间差异较大,会造成预计结果的偏差较大。为解决这一问题,提出了一种基于"功能—运动—动作"结构化分解的可靠性预计新方法。通过明确产品的各大运动功能建立功能层,分析完成各功能的机械部件运动建立运动层,确定实现各机械部件运动的元动作运动单元建立元动作层,建立了可靠性预计的"功能—运动—元动作运动单元"层次模型。引入区间层次分析法,确定了"功能—运动—元动作运动单元"层次模型中各子代运动单元对父代运动单元的可靠性影响权重,并构建了由子代运动单元预计父代运动单元可靠性的数学模型。确定影响评价机械结构相似度的因素,构建了区间数综合评判云模型并选取与元动作运动单元相似的参考结构,以区间数综合评价云模型得出的相似度值修正参考结构的可靠性数据来预计元动作运动单元的可靠性。将元动作运动单元的可靠性预计值代入数学模型,层层预计父代运动单元的可靠性,从而得出整个产品的可靠性水平。最后以数控转台为例进行了分析,得出了可靠性预计值,并将该方法与传统方法进行了比较。结果表明,新的可靠性预计方法具有较好的可行性和较高的准确性。     

Proline-derived in situ synthesis of nitrogen-doped porous carbon nanosheets with encaged Fe2O3@Fe3C nanoparticles for lithium-ion battery anodes

The homogeneous incorporation of heteroatoms into two-dimensional C nanostructures, which leads to an increased chemical reactivity and electrical conductivity as well as enhanced synergistic catalysis as a conductive matrix to disperse and encapsulate active nanocatalysts, is highly attractive and quite challenging. In this study, by using the natural and cheap hydrotropic amino acid proline—which has remarkably high solubility in water and a desirable N content of ~12.2 wt.%—as a C precursor pyrolyzed in the presence of a cubic KCl template, we developed a facile protocol for the large-scale production of N-doped C nanosheets with a hierarchically porous structure in a homogeneous dispersion. With concomitantly encapsulated and evenly spread Fe₂O₃ nanoparticles surrounded by two protective ultrathin layers of inner Fe₃C and outer onion-like C, the resulting N-doped graphitic C nanosheet hybrids (Fe₂O₃@Fe₃C-NGCNs) exhibited a very high Li-storage capacity and excellent rate capability with a reliable and prolonged cycle life. A reversible capacity as high as 857 mAh•g^–1 at a current density of 100 mA•g^–1 was observed even after 100 cycles. The capacity retention at a current density 10 times higher—1,000 mA•g^–1—reached 680 mAh•g^–1, which is 79% of that at 100 mA•g^–1, indicating that the hybrids are promising as anodes for advanced Li-ion batteries. The results highlight the importance of the heteroatomic dopant modification of the NGCNs host with tailored electronic and crystalline structures for competitive Li-storage features.