融合协同过滤的线性回归推荐算法

针对传统协同过滤算法中存在数据稀疏问题,提出融合协同过滤的线性回归推荐算法。根据用户对项目的评分以及用户和项目自身特征,构建用户间和项目间相似矩阵。基于相似矩阵,选出用户和项目最近邻集合,分别通过基于用户和基于项目的协同过滤算法来预测用户已评分项目的评分,将预测评分与真实评分的差值作为特征,组合在一起生成新的训练数据。把新的训练数据作为线性回归模型的输入,根据训练好的模型预测未知评分,采用Top-N算法产生推荐列表。在MovieLens数据集上进行实验。实验结果表明,新算法的推荐准确性较传统协同过滤算法有显著提高。     

Reliability-based robust assessment for multiobjective optimization design of improving occupant restraint system performance

Optimal performance of vehicle occupant restraint system (ORS) requires an accurate assessment of occupant injury values including head, neck and chest responses, etc. To provide a feasible framework for incorporating occupant injury characteristics into the ORS design schemes, this paper presents a reliability-based robust approach for the development of the ORS. The uncertainties of design variables are addressed and the general formulations of reliable and robust design are given in the optimization process. The ORS optimization is a highly nonlinear and large scale problem. In order to save the computational cost, an optimal sampling strategy is applied to generate sample points at the stage of design of experiment (DOE). Further, to efficiently obtain a robust approximation, the support vector regression (SVR) is suggested to construct the surrogate model in the vehicle ORS design process. The multiobjective particle swarm optimization (MPSO) algorithm is used for obtaining the Pareto optimal set with emphasis on resolving conflicting requirements from some of the objectives and the Monte Carlo simulation (MCS) method is applied to perform the reliability and robustness analysis. The differences of three different Pareto fronts of the deterministic, reliable and robust multiobjective optimization designs are compared and analyzed in this study. Finally, the reliability-based robust optimization result is verified by using sled system test. The result shows that the proposed reliability-based robust optimization design is efficient in solving ORS design optimization problems.     

Adaptive block size for dense QR factorization in hybrid CPU–GPU systems via statistical modeling

QR factorization is a computational kernel of scientific computing. How can the latest computer be used to accelerate this task? We investigate this topic by proposing a dense QR factorization algorithm with adaptive block sizes on a hybrid system that contains a central processing unit (CPU) and a graphic processing unit (GPU). To maximize the use of CPU and GPU, we develop an adaptive scheme that chooses block size at each iteration. The decision is based on statistical surrogate models of performance and an online monitor, which avoids unexpected occasional performance drops. We modify the highly optimized CPU–GPU based QR factorization in MAGMA to implement the proposed schemes. Numerical results suggest that our approaches are efficient and can lead to near-optimal block sizes. The proposed algorithm can be extended to other one-sided factorizations, such as LU and Cholesky factorizations.     

Application of a rice field experimental error distribution function to nitrogen-phosphorus-potassium fertilizer response model analysis

A distribution function of rice yield deviations from the mean was developed from field experiments with 555 plots at 16 sites in Zhejiang province, China, for three years. The deviation distribution in interval of 50kg/ha appeared as a symmetrical distribution with a high peak (Mean=0.279 [kg/ha], SD=240.686 [kg/ha]). Normality test using Kolmogrove-Smirnov test between the observed cumulative distribution and the normal cumulative distribution function indicates that the observed deviation distribution is not normal. An empirical exponential cumulative distribution function was developed. The distribution function was used to remove outliers during the development of a rice yield fertilizer response model, based on data from a non-replicated NPK field experiment.     

昭通横江流域年径流预测

针对昭通横江由南到北贯穿昭通全境,水资源量丰富,但南北差异大,南部少、北部多的特点,根据流域内水文站实测年径流、大气环流指数、海温指数及其他指数等资料系列,采用多元回归分析法拟合1981—2010年径流,建立了预测模型,验证、预测分析了2011—2016年径流量。结果表明:干流控制站预测精度高于干流上段及支流代表站,水利工程建设等人类活动影响是导致出现这样结果的主要原因;多元回归分析在横江流域径流预测中具有一定的适应性,但因指标的差异及相关参数可获取性的差异,预测结果存在区域间、等级间的差别。     

Real-time facial animation on mobile devices

We present a performance-based facial animation system capable of running on mobile devices at real-time frame rates. A key component of our system is a novel regression algorithm that accurately infers the facial motion parameters from 2D video frames of an ordinary web camera. Compared with the state-of-the-art facial shape regression algorithm [1], which takes a two-step procedure to track facial animations (i.e., first regressing the 3D positions of facial landmarks, and then computing the head poses and expression coefficients), we directly regress the head poses and expression coefficients. This one-step approach greatly reduces the dimension of the regression target and significantly improves the tracking performance while preserving the tracking accuracy. We further propose to collect the training images of the user under different lighting environments, and make use of the data to learn a user-specific regressor, which can robustly handle lighting changes that frequently occur when using mobile devices.     

化肥生产的几个主要因素与能耗的关系

分析广州石油化工总厂化肥厂１９８６～１９９６年的生产数据，得出能耗与合成氨和尿素装置的运转率及生产负荷率有显著或极显著的负相关性，建立了几种直线回归方程。分析表明负荷对能耗的影响远大于运转率的影响，能耗与装置停车的次数相关性不显著。     

数据挖掘预测模型在脑损伤患者认知功能康复中的应用

为了更好地预测后天性脑损伤ABI(Acquired Brain Injury)患者认知功能康复的影响因素,提出基于决策树(DT)、多层感知器(MLP)和广义回归神经网络(GRNN)的三种预测模型。借助于10折交叉验证测试算法,通过专一性、灵敏度和精度分析以及混淆矩阵分析对模型的性能进行测试,从而获得新的知识以评估和改善认知功能康复过程中的有效性。实验结果表明,基于DT的模型的模拟结果明显比其他模型更为优越,预测平均精度可高达90.38%。     

基于贪心核特征提取方法的中期峰值负荷预测

针对中期电力负荷预测, 提出基于贪心核主元回归(GKPCR)、贪心核岭回归(GKRR) 的特征提取建模方法. 通过对核矩阵的稀疏逼近, GKPCR和GKRR两种贪心核特征提取方法旨在寻找特征空间中数据的低维表示, 计算需求低, 适用于大数据集的在线学习. 将所提出的方法应用于不同地区的电力负荷中期峰值预测, 并与现有预测方法进行了比较. 实验结果表明, 在同等条件下, 所提出的方法能有效地改进预测精度, 而且性能更好, 显示了其有效性和应用潜力.

     

基于GP-SRCDKF的初始对准技术研究

随着对惯性导航系统中对准时间要求的不断提高,初始对准需要在大方位失准角条件下进行,此时需采用非线性滤波方法来实现初始对准。基于此,提出高斯过程回归平方根中心差分卡尔曼滤波算法(GP-SRCDKF)。将高斯过程回归融入到SRCDKF算法中,利用高斯过程得到系统回归模型及噪声协方差,用回归模型代替状态方程和观测方程,对相应的噪声协方差进行实时自适应调整。该算法不仅克服了扩展卡尔曼滤波滤波精度低、需要计算雅可比矩阵的不足,而且可解决传统滤波容易受系统动态模型不确定和噪声协方差不准确的限制。仿真实验结果验证了该算法的有效性和优越性。