基于量子重力探测技术的水下匹配导航仿真分析与评估

在电磁信号拒止的海洋环境中,基于海洋重力场辅助的水下匹配导航技术是提高潜航器导航定位精度的有效途径之一。近年来,基于冷原子干涉的量子绝对重力探测技术发展迅速,其探测精度高、可移动性强,在水下重力辅助导航领域具有极大的应用潜力。结合冷原子干涉量子重力仪最新技术发展水平,选取太平洋某典型海域,通过地形正演构建了高精度（<0.1mGal）、高分辨率（500m）海洋重力基准图,基于最近等值线迭代（Iterative Closest Contour Point,ICCP）算法实现了对潜航器水下航行误差的重力场辅助匹配校正,将初始航迹误差从约4km校正到约400m。匹配算法具有指数收敛优势,在阈值收敛误差为10-6下,算法平均收敛迭代次数44次,平均收敛时间0.0678s。通过仿真分析和“探测技术-重力场基准图-匹配算法”系统综合效能评估,论证了现有量子重力探测技术在重力辅助水下匹配导航领域的应用潜能。     

地铁站场景下的可行驶区域检测

在室内环境下的机器人视觉导航任务中,可行驶区域检测是不可或缺的一部分,这是保证自动驾驶任务实现的基础.目前较多的解决方法是对数据集中出现过的障碍物进行识别来检测可行驶区域,缺乏灵活性,因此本文提出了一种针对地铁站等室内平坦地面的可行驶区域检测方法,提高实用性.本文采用经典的MobileNetV3网络对采集到的前方图像进行分类,判断是否为地面区域.由于室内地面的地标、箭头等贴纸的影响,因此需要对非地面区域进一步判断,与常规的立体障碍物进行区分.本文利用连续帧之间的特征点匹配获得相机移动距离,并利用直线拟合计算斜率的方法达到区分立体障碍物与平面地标的目的.实验表明,本文提出的方法能较好地检测机器人前方可行驶区域,具有较高的实用价值.     

A matching game framework for users clustering and resource allocation with wireless power transfer in a CR-NOMA network

This paper investigates the resource allocation in a massively deployed user cognitive radio enabled non-orthogonal multiple access (CR-NOMA) network considering the downlink scenario. The system performance deteriorates with the number of users who are experiencing similar channel characteristics from the base station (BS) in NOMA. To address this challenge, we propose a framework for maximizing the system throughput that is based on one-to-one matching game theory integrated with the machine learning technique. The proposed approach is decomposed to solve users clustering and power allocation subproblems. The selection of optimal cluster heads (CHs) and their associated cluster members is based on Gale-Shapley matching game theoretical model with the application of Hungarian method. The CHs can harvest energy from the BS and transfer their surplus power to the primary user (PU) through wireless power transfer. In return, they are allowed to access the licensed band for secondary transmission. The power allocation to the users intended for power conservation at CHs is formulated as a probabilistic constraint, which is then solved by employing the support vector machine (SVM) algorithm. The simulation results demonstrate the efficacy of our proposed schemes that enable the CHs to transfer the residual power while ensuring maximum system throughput. The effects of different parameters on the performance are also studied.     

基于智能经纪人的新型电力双边交易模式

针对当前电力双边市场的交易效率和业务透明度不高的问题,提出智能经纪人促进电力双边交易的构想。梳理当前电力双边市场存在的主要问题,设计智能经纪人的功能理念;分析市场主体的潜在价格让步空间,研究考虑潜在交易前景的个性化协同撮合方法;构建基于智能经纪人的新型电力双边交易模式,并开发配套智能交易平台。以某省电力市场数据进行市场模拟,算例结果表明,智能经纪人可以降低双边交易成本以及提高市场成交率。     

基于高斯混合模型的海洋能发电资源优化配置研究

针对海洋能资源丰富的海岛电网,提出了以消纳率最优为目标的海洋能发电资源优化配置方法。首先,采用高斯混合模型描述海洋能发电的随机波动性,并生成出力场景;然后以海洋能消纳率最优为目标构建了海岛电网的海洋能发电资源优化配置模型;最后,对渗透率、消纳率及源荷匹配度三个指标进行综合分析,获得最优配置方案。基于实际海岛负荷、波浪和潮流数据构建了海洋能发电资源优化配置算例,并重点分析了资源优化配置过程中渗透率约束的取值范围。结果表明,在不同的渗透率约束下,源荷匹配度呈现先升后降的趋势。因此,可以根据最大的源荷匹配度,确定资源优化配置模型中的渗透率约束取值范围。此外,在最优渗透率约束取值范围内,高斯混合模型出力场景与实测数据场景的优化配置结果相差不大且趋势一致,验证了高斯混合模型生成海洋能出力场景的有效性。     

轨道角动量光束非线性转换研究进展北大核心CSCD

轨道角动量(OAM)是光的一个重要的自由度。由于携带OAM的光束具有特殊的强度相位分布以及力学效应,使得此类光束在高速光通信、测量、成像、光镊和量子信息中具有广泛的应用。关于OAM光束在准相位匹配晶体(QPM)中的频率变换研究,一方面可以研究OAM光束参与非线性相互作用时与高斯光束不同的物理机制;另一方面,非线性过程提供了多种有效的光场调控手段,可以实现携带OAM光场不同自由度的精细调控,为满足不同的光学应用奠定基础。综述了近十年来OAM光束在QPM晶体中的非线性转换研究主要进展,具体包括:非线性过程中OAM光束的守恒、传输、演化和干涉行为研究,高效率的OAM激光和单光子态频率转换研究,OAM频率转换效率模式非依赖性研究,矢量光束的频率转换研究,以及无后向选择的高维OAM纠缠态的制备研究。最后讨论和展望了OAM在QPM晶体中频率转换方面的未来研究趋势。     

A well‐conditioned and optimally convergent XFEM for 3D linear elastic fracture

A variation of the extended finite element method for three‐dimensional fracture mechanics is proposed. It utilizes a novel form of enrichment and point‐wise and integral matching of displacements of the standard and enriched elements in order to achieve higher accuracy, optimal convergence rates, and improved conditioning for two‐dimensional and three‐dimensional crack problems. A bespoke benchmark problem is introduced to determine the method's accuracy in the general three‐dimensional case where it is demonstrated that the proposed approach improves the accuracy and reduces the number of iterations required for the iterative solution of the resulting system of equations by 40% for moderately refined meshes and topological enrichment. Moreover, when a fixed enrichment volume is used, the number of iterations required grows at a rate which is reduced by a factor of 2 compared with standard extended finite element method, diminishing the number of iterations by almost one order of magnitude. Copyright © 2015 John Wiley & Sons, Ltd.     

双目立体视觉匹配技术现状与发展

双目立体视觉技术是计算机视觉重要的研究领域,如今的应用也愈发广泛,其中在精密测量上起到的作用不可或缺。匹配技术在双目测量中尤为关键。本文从立体匹配技术的现状出发,介绍了现阶段匹配技术运用的主要方式和关键算法,以基于基元的匹配算法入手,对该技术的发展趋势进行展望。通过对比各算法的优缺点,获得对匹配技术的进一步了解,为未来进行双目视觉测量的应用提供理论依据。     

Nucleation Crystallography of Ni Grains on CrFeNb Inoculants Investigated by Edge‐to‐Edge Matching Model in an IN718 Superalloy

In this work, the nucleation crystallography of CrFeNb intermetallic particles as a grain refiner for Ni‐based IN718 superalloys is studied using Edge‐to‐Edge Matching model. Three distinguishable orientation relationships between CrFeNb intermetallic particles and Ni grains are well predicted: , (111)_Ni 1.28° from (0004)_CrFeNb, , (111)_Ni 1.32° from _CrFeNb, and _Ni//[0001]_CrFeNb, (111)_Ni 0.72° from _CrFeNb. The results indicate that CrFeNb intermetallic particles have a strong nucleation potency as an effective grain refiner for Ni‐based superalloy and the existence of semi‐coherent interfaces between the CrFeNb intermetallic particles and Ni grains. Furthermore, the IN718 superalloy is used to experimentally validate the grain refinement effect of CrFeNb intermetallic particles, showing that its grain size is obviously refined from 8.60 to 1.23 mm. And, the corresponding heterogeneous nucleation mechanism of grain refinement at the atomic level is further identified.

     

A new methodology for Bayesian history matching using parallel interacting Markov chain Monte Carlo

This paper presents an innovative application of a new class of parallel interacting Markov chains Monte Carlo to solve the Bayesian history matching (BHM) problem. BHM consists of sampling a posterior distribution given by the Bayesian theorem. Markov chain Monte Carlo (MCMC) is well suited for sampling, in principle, any type of distribution; however the number of iteration required by the traditional single-chain MCMC can be prohibitive in BHM applications. Furthermore, history matching is typically a highly nonlinear inverse problem, which leads in very complex posterior distributions, characterized by many separated modes. Therefore, single chain can be trapped into a local mode. Parallel interacting chains is an interesting way to overcome this problem, as shown in this paper. In addition, we presented new approaches to define starting points for the parallel chains. For validation purposes, the proposed methodology is firstly applied in a simple but challenging cross section reservoir model with many modes in the posterior distribution. Afterwards, the application to a realistic case integrated to geostatistical modelling is also presented. The results showed that the combination of parallel interacting chain with the capabilities of distributed computing commonly available nowadays is very promising to solve the BHM problem.