油茶种植技术及发展前景探究

文章对油茶生产技术进行了详细的分析,对提升油茶种植技术和管理水平进行了探索,并集中讨论了油茶生产前景及有效的发展框架,为油茶产业发展提高了宝贵的经验。     

基于多线结构光的弱纹理物体形貌测量方法

大多数现有的主流结构光测量装置在被测物体的表面上投射单条激光,通过移动装置实现物体表面的扫描重建。为了实现物体表面的在线快速重建,将多线结构光和双目立体视觉理论相结合,设计了一种新的三维测量方法。提出了一种结合形态滤波和Zhang-Suen细化算法的光条骨架提取方法和基于光条序列关系的光条匹配算法,并通过极线约束实现光条上特征点的精确匹配。通过实验验证了方法的有效性,相对误差在3%以内。     

Design,analysis, and neural control of a bionic parallel mechanism

Frontiers of Mechanical Engineering - Although the torso plays an important role in the movement coordination and versatile locomotion of mammals, the structural design and neuromechanical control...     

Fundamental Evaluation of Power Supply and Rectifiers for Wireless Power Transfer Using Magnetic Resonant Coupling

This paper provides a fundamental analysis of a power supply and rectifiers for wireless power transfer using magnetic resonant coupling (MRC). MRC enables efficient wireless power transfer over middle‐range transfer distances. MRC for wireless power transfer should operate at a high frequency in the industry science medical band, such as 13.56 MHz, because the size of the transfer device decreases at higher transfer frequencies. Therefore, the output frequency of the power supply on the transmitting side should be 13.56 MHz. In addition, the rectifier on the receiving side is operated at a high frequency. This paper focuses on the reflected power in the power supply and rectifiers. Thus, the parametric design method is clarified for the power supply, including a low‐pass filter to match the output, the impedance of the power supply with the characteristic impedance of the transmission line. In addition, the effects on the rectifiers of silicon carbide and gallium nitride diodes are confirmed by performing an experiment and a loss analysis.     

一种适于HTTP数据还原的QS改进算法

为了更快速、准确地对HTTP应用数据进行还原,文中研究了改进的单模式匹配算法。对BM算法、BMH算法和QS算法进行了分析,并重点研究了QS算法的改进思路,最后提出了一种适用于HTTP应用数据还原的CIQS算法。CIQS算法考虑了HTTP模式串的字符特点,改进了模式串的字符比较顺序,并对坏字符跳跃思想进行了改进,增大了跳跃距离。实验结果表明,CIQS算法有效减少了匹配次数,相比其他几种算法有更好的时间性能。     

Fault‐tolerant control of distributed systems by information pattern reconfiguration

Given a fault‐tolerance strategy and a distributed control system, the set of recoverable faults depends on the information pattern that is implemented. This paper shows that fault‐tolerance can be achieved, when possible, by the reconfiguration of the information pattern. A necessary and sufficient recoverability condition is provided, and sub‐optimal solutions are developed for actuator and sensor faults under system reconfiguration. Copyright © 2014 John Wiley & Sons, Ltd.     

GCAT-U-Net嵌入全局坐标注意力机制的遥感地块分割网络

遥感影像的地块背景特征复杂,当前地块分割方法不能较好地处理模糊的边缘信息,导致分割精度不理想;文章利用注意力机制处理地块特征,提出了一种基于全局坐标注意力机制的遥感地块分割网络:GCAT-U-Net;该方法在U-Net网络基础上嵌入了全局坐标注意力机制,加强了深度神经网络对于遥感影像数据中重要特征的关注度;在公开的GID数据集上的实验结果表明,文章提出的模型将准确率从0.9041提升到了0.9227,比传统U-Net网络提高了2百分点;结合特征自身重要性和特征位置信息的全局坐标注意力机制有助于更精确的目标定位,其输出相较于嵌入单一注意力机制,地块边界更为清晰,提升效果更为显著。     

Structure-sensitive semantic matching for aggregate question answering over knowledge base

Aggregate question answering essentially returns answers for given questions by obtaining query graphs with unique dependencies between values and corresponding objects. Word order dependency, as the key to uniquely identify dependency of the query graph, reflects the dependencies between the words in the question. However, due to the semantic gap caused by the expression difference between questions encoded with word vectors and query graphs represented with logical formal elements, it is not trivial to match the correct query graph for the question. Most existing approaches design more expressive query graphs for complex questions and rank them just by directly calculating their similarities, ignoring the semantic gap between them. In this paper, we propose a novel Structure-sensitive Semantic Matching(SSM) approach that learns aligned representations of dependencies in questions and query graphs to eliminate their gap. First, we propose a cross-structure matching module to bridge the gap between two modalities(i.e., textual question and query graph). Then, we propose an entropy-based gated AQG filter to remove the structural noise caused by the uncertainty of dependencies. Finally, we present a two-channel query graph representation that fuses the semantics of abstract structure and grounding content of the query graph explicitly. Experimental results show that SSM could learn aligned representations of questions and query graphs to eliminate the gaps between their dependencies, and improves up to 12% (F1 score) on aggregation questions of two benchmark datasets.     

Extracting Low-Frequency Spatio-Temporal Patterns in Ambient Power System Data Using Blind Source Separation

Wide-area techniques provide a powerful tool to extract spatio-temporal patterns from high-dimensional datasets and can be used for event detection and visualization, data fusion, stability assessment, and coherency analysis. In this paper, a novel blind source separation-based approach for extracting low-frequency spatio-temporal patterns from measured ambient power system data is proposed and a spatio-temporal visualization index is also suggested. This methodology combines a nonlinear hierarchical neural network with a Blind Source Separation (BSS) technique. The neural network allows reducing noise and removing the nonlinear relations among data (preserve dynamic features of interest), while the BSS technique permits extracting spatial and temporal patterns. In addition, the proposed approach takes advantage of the latest techniques in nonlinear estimation of non-stationary time series. Finally, application examples of the proposed framework on real test cases recorded from an actual power system by Phasor Measurement Units (PMUs) are presented. The obtained results show that the temporal patterns can be used for extracting and identifying the low-frequency oscillation modes and the spatial patterns can be used for identifying modes with the most contribution in original data. Compared to other BSS approaches, the proposed method has shown to be better for the analysis of real ambient data.     

DEM: Deep Entity Matching Across Heterogeneous Information Networks

Heterogeneous information networks, which consist of multi-typed vertices representing objects and multi-typed edges representing relations between objects, are ubiquitous in the real world. In this paper, we study the problem of entity matching for heterogeneous information networks based on distributed network embedding and multi-layer perceptron with a highway network, and we propose a new method named DEM short for Deep Entity Matching. In contrast to the traditional entity matching methods, DEM utilizes the multi-layer perceptron with a highway network to explore the hidden relations to improve the performance of matching. Importantly, we incorporate DEM with the network embedding methodology, enabling highly efficient computing in a vectorized manner. DEM’s generic modeling of both the network structure and the entity attributes enables it to model various heterogeneous information networks flexibly. To illustrate its functionality, we apply the DEM algorithm to two real-world entity matching applications: user linkage under the social network analysis scenario that predicts the same or matched users in different social platforms and record linkage that predicts the same or matched records in different citation networks. Extensive experiments on real-world datasets demonstrate DEM’s effectiveness and rationality.