基于数据质量评价的目标估计

针对目标估计过程需要大量人工参与、自动化程度低的问题，提出了基于数据质量评价的目标估计方法。利用目标数据质量评价方法，对不同传感器得到的目标数据质量进行科学、有效的测度和评价，并根据质量得分动态调整各数据源在目标估计过程中所占的权重，从而减少人工干预，提高目标估计效能。仿真试验结果证明了该方法的有效性。     

基于改进MobileFaceNet的人脸识别方法北大核心

针对目前大多数人脸识别算法参数多、计算量大,难以部署到移动端和嵌入式设备中的问题,提出了一种基于改进MobileFaceNet的人脸识别方法。通过对MobileFaceNet模型结构的调整,将bottleneck模块优化为sandglass模块,改良深度卷积和逐点卷积的相对位置,适当增大sandglass模块的输出通道数,从而减少特征压缩时的信息丢失,增强人脸空间特征的提取。实验结果表明:改进后的方法在LFW测试数据集上准确率达99.15%,模型大小和计算量分别仅为原算法的61%和45%,验证了所提方法的有效性。     

Mutual information maximizing GAN inversion for real face with identity preservation

Recent generative adversarial networks (GANs) have yielded remarkable performance in face image synthesis. GAN inversion embeds an image into the latent space of a pretrained generator, enabling it to be used for real face manipulation. However, current inversion approaches for real faces suffer the dilemma of initialization collapse and identity loss. In this paper, we propose a hierarchical GAN inversion for real faces with identity preservation based on mutual information maximization. We first use a facial domain guaranteed initialization to avoid the initialization collapse. Furthermore, we prove that maximizing the mutual information between inverted faces and their identities is equivalent to minimizing the distance between identity features from inverted and original faces. Optimization for real face inversion with identity preservation is implemented on this mutual information-maximizing constraint. Extensive experimental results show that our approach outperforms state-of-the-art solutions for inverting and editing real faces, particularly in terms of face identity preservation.     

控制爆破法处理卡钻事故

介绍了在包头市某工程实施管道穿越黄河施工中,采用爆破法处理卡钻的经验。针对深水环境条件及钻杆内径小不宜采用集团装药的条件,确定采用"小直径爆破筒,钻杆内部装药"的爆破方案,阐述了爆破设计及施工注意事项。可供类似工程参考。     

针对细微特征进行K-means聚类的电台分选识别技术

现代战场中的无线通信设备日益增多，精准获取个体信息已成为研究热点，但也是难点。针对通信电台，提出了一种分选识别技术。该技术从电台物理层特性出发，对其辐射信号的细微特征进行K-means聚类以实现分选，分选的同时提取各个个体的特征属性值，未知信号通过与特征属性值相关运算实现个体识别。该技术无需先验知识，无需训练运算，通过实验验证，其可行、高效，易于工程实现。     

IRS辅助的SWIPT系统中基于能效优先的波束成形设计与优化

以智能反射面(intelligent reflecting surface,IRS)辅助的无线携能通信(simultaneous wireless information and power transfer,SWIPT)系统为背景,研究了该系统中基于能效优先的多天线发送端有源波束成形与IRS无源波束成形联合设计与优化方法。以最大化接收端的最小能效为优化目标,构造在发送端功率、接收端能量阈值、IRS相移等多约束下的非线性优化问题,用交替方向乘子法(alternating direction method of multipliers,ADMM)求解。采用Dinkelbach算法转化目标函数,通过奇异值分解(singular value decomposition,SVD)和半定松弛(semi-definite relaxation,SDR)得到发送端有源波束成形向量。采用SDR得到IRS相移矩阵与反射波束成形向量。结果表明,该系统显著降低了系统能量收集(energy harvesting,EH)接收端的能量阈值。当系统总电路功耗为?15 dBm时,所提方案的用户能效为300 KB/J。当IRS反射阵源数与发送天线数均为最大值时,系统可达最大能效。     

Locating wind farm for power and hydrogen production based on Geographic information system and multi-criteria decision making method: An application

Hydrogen generation from renewable energy resources is considered as a suitable solution to solve the problems related to the energy sector and the reduction of greenhouse gases. The aim of this study is to provide an integrated framework for identifying suitable areas for the construction of wind farms to produce hydrogen. For this purpose, a combined method of Geographic Information System (GIS) and multi-criteria decision making (MCDM) has been used to locate the power plant in Yazd province. The GIS method in the present study consisted of two parts: constraints and criteria. The constraint section included areas that were unsuitable for the construction of wind farms to produce power and hydrogen. In the present study, various aspects such as physical, economic and environmental had been considered as constraints. In the criteria section, eight different criteria from technical aspects (including average wind speed, hydrogen production potential, land slope) and economic aspects (including distance to electricity grid, distance to urban areas, distance to road, distance to railway and distance to centers of High hydrogen consumption) had been investigated. The MCDM tool had been used to weigh the criteria and identify suitable areas. Analytic Hierarchy Process (AHP) technique was used for weighting the criteria. The results of AHP weighting method showed that economic criteria had the highest importance with a value of 0.681. The most significant sub-criterion was the distance to urban areas and the least significant sub-criterion was the distance to power transmission lines. The results of GIS-MCDM analysis had shown that the most proper areas were in the southern and central sectors of Yazd province. In addition, the feasibility of hydrogen production from wind energy had shown that this province had the capacity to generate hydrogen at the rate of 53.6–128.6 tons per year.     

国际橄榄理事会的定位和使命及相关标准（网络首发、推荐阅读）

The International Olive Council(IOC) is an international intergovernmental organization dedicated to olive oil and table olives, aiming at modernizing olive production, coordinating olive policies, improving the regulation of international trade, defending the quality of the olive sector and promoting olive oil and table olives to increase their consumption. The IOC grants recognition of laboratories and tasting panels in annual trials when they meet the conditions given in the decisions adopting the IOC certificate for laboratories for the physico–chemical testing of olive oil and laboratories for the sensory analysis of virgin olive oils. The IOC establishes analysis methods applying to olive oils and olive pomace oils for purity, quality and organoleptic assessment. The IOC elaborates guides of storage conditions for olive oils and olive pomace oils, of managing virgin olive oil tasting panels and of sensory testing laboratories. In future works, the IOC includes activities to identify analytical criteria for detecting fraud and guaranteeing the quality of olive oils and olive pomace oils.     

A Tradeoff Between Accuracy and Speed for K-Means Seed Determination

With a sharp increase in the information volume, analyzing and retrieving this vast data volume is much more essential than ever. One of the main techniques that would be beneficial in this regard is called the Clustering method. Clustering aims to classify objects so that all objects within a cluster have similar features while other objects in different clusters are as distinct as possible. One of the most widely used clustering algorithms with the well and approved performance in different applications is the k-means algorithm. The main problem of the k-means algorithm is its performance which can be directly affected by the selection in the primary clusters. Lack of attention to this crucial issue has consequences such as creating empty clusters and decreasing the convergence time. Besides, the selection of appropriate initial seeds can reduce the cluster’s inconsistency. In this paper, we present a new method to determine the initial seeds of the k-mean algorithm to improve the accuracy and decrease the number of iterations of the algorithm. For this purpose, a new method is proposed considering the average distance between objects to determine the initial seeds. Our method attempts to provide a proper tradeoff between the accuracy and speed of the clustering algorithm. The experimental results showed that our proposed approach outperforms the Chithra with 1.7% and 2.1% in terms of clustering accuracy for Wine and Abalone detection data, respectively. Furthermore, achieved results indicate that comparing with the Reverse Nearest Neighbor (RNN) search approach, the proposed method has a higher convergence speed.     

基于音频特征的工程车辆工况识别研究

高效率地使用工程车辆是工程项目管理中节约成本的有效方法，无人监管环境下工程车辆的工况识别，是实现工程车辆高效率使用的有效手段。目前以GPS等技术为核心的车辆智能管理系统未对工程车辆进行工况识别，提出一种基于GRU循环神经网络的工程车辆工况识别方法，通过对工程车辆在不同工况下产生的音频信号进行分析，从中提取Mel倒谱系数作为主要特征，构建GRU循环神经网络模型进行训练和识别。实验结果表明，该方法可以实现对工程车辆工况的有效识别。