kernel

摘要： In this paper, an optimization model is proposed to simulate and predict the current situation of smog. The model takes the interval grey number sequence with the known possibility function as the original data, and constructs a time-delay nonlinear multivariable grey model MGM based on the new kernel and degree of greyness sequences considering its time-delay and nonlinearity. The time-delay parameter is determined by the maximum value of the grey time-delay absolute correlation degree, and the nonlinear parameter is determined by the minimum value of average relative error. In order to verify the feasibility of the model, this paper uses the smog related data of Nanjing city for simulation and prediction. Compared with the other four models, the new model has higher simulation and prediction accuracy.

摘要： Data-driven paradigms are well-known and salient demands of future wireless communication. Empowered by big data and machine learning techniques,next-generation data-driven communication systems will be intelligent with unique characteristics of expressiveness, scalability, interpretability, and uncertainty awareness, which can confidently involve diversified latent demands and personalized services in the foreseeable future. In this paper, we review a promising family of nonparametric Bayesian machine learning models,i.e., Gaussian processes(GPs), and their applications in wireless communication. Since GP models demonstrate outstanding expressive and interpretable learning ability with uncertainty, they are particularly suitable for wireless communication. Moreover, they provide a natural framework for collaborating data and empirical models(DEM). Specifically, we first envision three-level motivations of data-driven wireless communication using GP models. Then, we present the background of the GPs in terms of covariance structure and model inference. The expressiveness of the GP model using various interpretable kernels, including stationary, non-stationary, deep and multi-task kernels,is showcased. Furthermore, we review the distributed GP models with promising scalability, which is suitable for applications in wireless networks with a large number of distributed edge devices. Finally, we list representative solutions and promising techniques that adopt GP models in various wireless communication applications.

摘要： The purpose of relation extraction is to identify the semantic relations between entities in sentences that contain two entities.Recently,many variants of the convolution neural network(CNN)have been introduced to relation extraction for the extracting of features--the quality of the neural network model directly affects the final quality of relation extraction.However,the traditional convolution network uses a fixed convolution kernel,so it is difficult to choose the size of the convolution kernel dynamically,which results in networks with weak representation ability.To address this,a novel CNN is designed with selective kernel networks and multigranularity.In the process of feature extraction,the model can adaptively select the size of the convolution kernel,that is,give more weight to the appropriate convolution kernel.It is then combined with multigranularity convolution to obtain more abundant semantic information.Finally,a new pooling method is designed to obtain more comprehensive information and improve model performance.Experimental results indicate that this method is effective without excessively deep network layers,and it also outperforms several competitive baseline methods.

摘要： Dear editor,Recently, researchers have discovered a new kind of attack named Meltdown [1] that exploits the out-of-order execution behavior of modern processors and side-channels. The adversary can use this method to overcome the memory isolation that is protected by a processor and read the entire kernel memory of a machine it executes on. Mitigating this new attack is thought to be expensive at the moment because current solutions are based on common optimization techniques for modern processors.

摘要： 构建工业转型升级评价指标体系,实证测度了中国30个省(区、市)2003-2017年工业转型升级水平状况,并采用Dagum基尼系数和Kernel核密度估计两种方法对工业转型升级的地区差距和动态演化进行了具体研究.研究表明:考察期内,中国工业转型升级取得较好成效,工业转型升级指数从2003年0.3734上升至2017年0.5216,年均提高2.42％;总体地区差距较小,基尼系数介于0.0776-0.1085,虽存在明显的区域差距,但总体比较平衡;从东中西部三大地区的基尼系数及核密度估计看,西部地区工业转型升级的地区差距最大,东部与中部地区相当.为了推动中国工业转型升级,一方面各地方政府应继续将提升工业转型升级水平作为一个统一的目标协同推进,另一方面应该从缩小地区内和地区间差距着手推进工业转型升级的区域协同提升.

摘要： Reliability analysis is the key to evaluate software’s quality. Since the early 1970s, the Power Law Process, among others, has been used to assess the rate of change of software reliability as time-varying function by using its intensity function. The Bayesian analysis applicability to the Power Law Process is justified using real software failure times. The choice of a loss function is an important entity of the Bayesian settings. The analytical estimate of likelihood-based Bayesian reliability estimates of the Power Law Process under the squared error and Higgins-Tsokos loss functions were obtained for different prior knowledge of its key parameter. As a result of a simulation analysis and using real data, the Bayesian reliability estimate under the Higgins-Tsokos loss function not only is robust as the Bayesian reliability estimate under the squared error loss function but also performed better, where both are superior to the maximum likelihood reliability estimate. A sensitivity analysis resulted in the Bayesian estimate of the reliability function being sensitive to the prior, whether parametric or non-parametric, and to the loss function. An interactive user interface application was additionally developed using Wolfram language to compute and visualize the Bayesian and maximum likelihood estimates of the intensity and reliability functions of the Power Law Process for a given data.

摘要： Quadratic distance estimation making use of the sample quantile function over a continuous range is introduced. It extends previous methods which are based only on a few sample quantiles and it parallels the continuous GMM method. Asymptotic properties are established for the continuous quadratic distance estimators (CQDE) and the implementation of the methods are discussed. The methods appear to be useful for balancing robustness and efficiency and useful for fitting distribution with model quantile function being simpler than its density function or distribution function.

摘要： 在系统运维过程中发现某服务器因为可用内存不足,经常出现应用意外停止情况.通过系统内核调整,增加了系统可用内存,成功解决了MQ异常终止问题,增强了系统稳定性.因此,本文针对32位Linux系统可用内存不足问题及处理方法进行阐述,试图为之提供行之有效的可行性建议.

摘要： The steam turbine is a prime mover that converts kinetic energy in steam into rotational mechanical energy through the impact or reaction of the steam against the blades. The aim of this study is to design a steam turbine for a small scale steam power plant with target of producing electricity. The turbine is driven by the heat energy from palm kernel shells as a renewable energy source obtained at a lower or no cost. The study was concentrated on design of turbine elements and its validation using computer packages. Specifically, the microturbine design was limited to design, modeling, simulation and analysis of the rotor, blades and nozzle under the palm kernel shell as fuel for the micro power plant. In blade design, stress failures, efficiency and blade angle parameters were considered. In casing volume design, the overall heat transfer and mean temperature, and different concepts were applied. The thermal distribution on stator and rotor was considered in order to determine its level of tolerance. The design software packages used for design validation were Solidworks and Comsol Multiphysics for analysis. Simulation results showed that the designed steam turbine can adequately tolerate change in stress/load, torsion/compression, temperature and speeds.

摘要： 本发明公开了一种基于Kernel‑CNN的心电信号识别分类方法，将核变换引入卷积过程构成核变换卷积操作，进一步增强模型特征提取的能力；并将其通过在美国麻省理工学院提供的MIT‑BIH数据库中提供的数据进行验证，结果表明本发明模型相比于卷积神经网络在相同预测正确率上有较低的LOSS值。本发明的卷积神经网络具有出色特征提取能力，通过将核变换引入卷积操作，实现数据的非线性映射，进一步增强了卷积过程特征提取的能力。心电信号输入训练好的网络之后，可以得到五种分类的概率值，选择概率值最大的作为该数据所属类型。不需要信号先验知识，也不需要专家的输入，可以从心电信号中提取有效的特征，可应用于医疗器械对心电图的识别并分类。

摘要： 本发明提出一种基于Kernel采样策略在不确定性环境下的在线规划方法，用于对机器人执行任务时的规划，在该不确定性环境中，表示为POMDP模型的不确定性是制约机器人可靠运行的主因；所述POMDP模型中，机器人可观测自身的部分状态，机器人通过不断的与环境进行交互来获得回报最大的策略；在所述在线规划方法中，处理可观测部分时，把机器人的状态表示为一个信念，记为belief，其属于一个状态的集合，以POMDP算法通过构建信念树的方式执行前向搜索，以此来获得当前信念下的最优策略；所述信念树的每一个节点代表一个信念，父节点与子节点通过行为‑观测分支连接；所述POMDP算法是在线POMDP规划算法Kernel‑DESPOT；本发明算法性能优于DESPOT和POMCP，在收敛速度以及质量上具有优势。

摘要： 本发明涉及一种基于改进Kernel方法结合稀疏约束的PET图像重建方法，包括以下步骤：获取PET原始数据和与PET配准好的MR图像；利用分割的MR图像构建各类组织的强度Kernel matrix和梯度Kernel matrix；结合强度Kernel matrix和梯度Kernel matrix构建各类组织的组织Kernel matrix；结合所有组织Kernel matrix构建成像物体的完整Kernel matrix；利用成像物体的完整Kernel matrix结合MLEM方法，得到基于改进Kernel方法重建的PET图像；增加稀疏约束，得到优化的PET图像。与现有技术相比，本发明改进了基于Kernel的PET重建方法中Kernel矩阵的构成方法，降低了图像重建的难度，提高了图像的分辨率，使重建的PET图像边缘更清晰；增加稀疏约束项来恢复PET独有的特征以及先验图像与重建PET的不匹配部分，解决MR作为先验与PET图像不完全匹配的问题，重建结果更好。

摘要： 本发明实施例提供一种Kernel代码及其上层代码的同步处理方法及装置。本发明实施例通过获取项目属性中第一控制字段的值，所述第一控制字段用于控制上层代码，根据所述第一控制字段的值设置Kernel代码的属性配置的值，以使所述第一控制字段的值和所述Kernel代码的属性配置的值一致，从而实现了上层代码和Kernel代码的同步，而无需开发人员手动修改上层代码和Kernel代码，以使上层代码和Kernel代码同步，节省了开发人员的工作量；另外，当上层代码被修改时，Kernel代码也会被同步修改，避免了源代码在编译或执行时出现问题。

摘要： 本发明公开了一种基于Kernel合并的GPU多任务调度系统及方法。包括任务分析模块、Kernel合并模块、任务调度模块和任务存储队列。用户提交GPU任务至任务存储队列，任务分析模块计算任务间的合并加速比，任务调度模块计算最优的合并调度顺序，并交至Kernel合并模块合并执行，获得最大任务吞吐率；本发明采用一般图的最大匹配计算最优合并调度顺序。通过任务分析模块计算任务的合并加速比；其次根据任务合并加速比构造无向图；通过二分图匹配算法计算最优的任务合并调度顺序；根据顺序调度任务组完成任务计算。本发明完成了上述功能的设计细节、实现算法和编码工作，提升了多任务下GPU的资源利用率。

摘要： 本发明涉及一种基于改进Kernel方法结合稀疏约束的PET图像重建方法，包括以下步骤：获取PET原始数据和与PET配准好的MR图像；利用分割的MR图像构建各类组织的强度Kernel matrix和梯度Kernel matrix；结合强度Kernel matrix和梯度Kernel matrix构建各类组织的组织Kernel matrix；结合所有组织Kernel matrix构建成像物体的完整Kernel matrix；利用成像物体的完整Kernel matrix结合MLEM方法，得到基于改进Kernel方法重建的PET图像；增加稀疏约束，得到优化的PET图像。与现有技术相比，本发明改进了基于Kernel的PET重建方法中Kernel矩阵的构成方法，降低了图像重建的难度，提高了图像的分辨率，使重建的PET图像边缘更清晰；增加稀疏约束项来恢复PET独有的特征以及先验图像与重建PET的不匹配部分，解决MR作为先验与PET图像不完全匹配的问题，重建结果更好。

摘要： 本发明公开了一种基于二维Kernel DM+V算法的稳态环境参数场重建优化算法，该算法属于环境监测、控制领域，目的在于解决空间中环境参数稳态分布的可视化问题。该算法利用局部风信息优化了测量点处环境参数对周围环境的影响权重。值得说明的是：本算法解决了过去Kernel DM+V/W算法在测量点上风区和下风区权重分配一致的问题，明显改善了重建环境参数场的效果，并提供了一种利用局部风信息的新思路。本发明与利用传统的应用CFD仿真模拟环境参数场相比无需考虑简化问题，也不需要精确的边界条件，并且计算速度大幅度提高，能实现可视化的稳态分布空间环境参数。

摘要： 本发明提出一种基于Kernel采样策略在不确定性环境下的在线规划方法，用于对机器人执行任务时的规划，在该不确定性环境中，表示为POMDP模型的不确定性是制约机器人可靠运行的主因；所述POMDP模型中，机器人可观测自身的部分状态，机器人通过不断的与环境进行交互来获得回报最大的策略；在所述在线规划方法中，处理可观测部分时，把机器人的状态表示为一个信念，记为belief，其属于一个状态的集合，以POMDP算法通过构建信念树的方式执行前向搜索，以此来获得当前信念下的最优策略；所述信念树的每一个节点代表一个信念，父节点与子节点通过行为‑观测分支连接；所述POMDP算法是在线POMDP规划算法Kernel‑DESPOT；本发明算法性能优于DESPOT和POMCP，在收敛速度以及质量上具有优势。

摘要： 本发明公开了一种基于改进的二维稳态Kernel DM+V/W的分布地图构建方法，包括以下步骤：1)根据预测的环境平面划分网格单元；2)通过计算优化的高斯核函数，得到每个环境参数传感器对每个网格单元的权重值；3)将每个网格单元的所有权重值进行累加，得到每个网格单元的综合权重值；4)计算每个网格单元的积分加权值；5)计算每个网格单元的置信值；6)计算每个网格单元的预估均值，得到均值在整个区域内的分布状况；7)为了更好的指示高波动区域，计算出每个网格单元的方差值，完成分布地图构建。本发明提供的基于改进的二维稳态Kernel DM+V/W的分布地图构建方法在相同环境参数传感器数量和布置方式下，更加充分的利用局部风速信息，提高了分布地图构建的准确性。

摘要： 本发明涉及计算机网络的技术领域，特别是涉及一种高性能Kernel‑Bypass透明代理系统，其通过提升硬件性能线性的提升代理系统的性能；包括多进程模式下的数据分流和使用用户态协议栈的透明代理。