Adaptive weighted least square support vector machine regression integrated with outlier detection and its application in QSAR

In order to eliminate the influence of unavoidable outliers in training sample on a model's performance, a novel least square support vector machine regression, which combines outlier detection approach and adaptive weight value for the training sample, is proposed and named as adaptive weighted least square support vector machine regression (AWLS-SVM). Firstly, the effective robust 3σ principle is used to detect marked outliers for the training sample. Secondly, based on the training sample without marked outliers, least square support vector machine regression is employed to develop the model and the fitting error of each sample data is obtained. Thirdly, according to the fitting error of each sample data, the initial weight is calculated. The bigger the fitting error of sample data is, the smaller the weight value of the sample data. Thus, the potential outliers, which are not detected by the robust 3σ principle and have bigger fitting errors, have smaller weight values to reduce the influence of the potential outliers on the performance of model. Then, LS-SVM is applied for the weighted sample to develop the model again. Finally, via the proposed weight value iterative method, the weight values of the training sample are converged, and the model with good predicting performance is obtained. To illustrate the performance of AWLS-SVM, simulation experiment is designed to produce the training sample with marked outlier and some non-marked outliers. AWLS-SVM, AWLS-SVM without the robust 3σ principle, LS-SVM with the robust 3σ principle, LS-SVM, and radial basis function network are applied to develop the model based on the designed sample. The results show that the influence of marked and un-marked outliers on the model's performance is eliminated by AWLS-SVM, and that the predicting performance of AWLS-SVM is the best. Furthermore, the AWLS-SVM method was applied to develop the quantitative structure–activity relationships (QSAR) model of HIV-1 protease inhibitors, and the satisfactory result was obtained.     

自适应误差惩罚支撑向量回归机

该文提出一种支撑向量回归机AEPSVR。它首先用 -SVR求得一个近似的支撑向量回归函数,在此基础上,引入一种新自适应误差惩罚函数,通过迭代,得到鲁棒的支撑向量回归机。该方法因以 -SVR为基础,故可以应用各种求解SVR的优化算法。实验表明,该支撑向量回归机AEPSVR能显著地降低离群点的影响,具有良好的泛化性能。     

Hybrid:一种两阶段的聚类算法

提出了一种两阶段的聚类方法：Hybrid。第一阶段产生大小相同的圆形原子聚类;第二阶段合并原子聚类形成任意形状和大小的聚合聚类。在扩展边界时,不但考虑原子聚类间的距离,还考虑原子聚类的密度相似度。这样可以更好地排除“噪音”的影响,得到内部结构更加趋同的聚合聚类。     

导航卫星载荷分系统健康评估方法设计

面向下一代导航系统结合高中低轨构建导航星座的设想,随着遥测参数数量和种类的激增,针对传统健康评估方法面临的过往专家知识难以适用、故障机理储备难以覆盖全面的问题,提出了基于局部异常因子检测-贝叶斯网络结构学习的导航卫星载荷分系统健康评估方法;通过采集某卫星系统实际故障时间点前后数据,设计实验验证了局部异常因子检测方法能够以粗粒度正确输出单机级的健康状况;分析比较了三种评分函数下,贝叶斯结构学习的效率和模型的准确度;实验结果表明,当评分函数分别选为BDeuScore、 K2Score以及BicScore时,学习到的模型对系统的健康评估准确度分别为87.4%、80.5%和85.2%;总结了局部异常因子检测-贝叶斯网络结构学习方法各自的不足,为导航卫星分系统健康评估方法提供了新方向和思路。     

浅谈狄克逊准则的速记方法及运用

在检定检测工作中使用仪器仪表的测量结果作为参考,但是由于仪器仪表本身的限制和人员操作、环境因素等的影响,测量准确度只能在一定的区间范围内。在计量学中,为了保证测量结果的准确,往往取多次测量的平均值作为测量结果。但是重复测量中,由于众多因素影响,仪器仪表也可能给出并不符合预期目的离群值。如果我们把这些数据值和正常数据值放在一起进行统计,可能会影响实验结果的正确性,如果把这些数据值简单地剔除,又可能忽略了重要的实验信息。     

Performance evaluation of outlier detection methods in GNSS vector networks using 1D and 3D component analysis

In most surveying studies, the computation of reference point coordinates in three-dimensional (3D) space has become inevitable with the extensive use of the Global Positioning System (GPS). Such computations are particularly required in the establishment and densification of 3D geodetic networks. In contrast to the classical measurements, GPS vector measurements, resulting from the processing of code and phase observations, are physically correlated. Therefore, in the analysis of observations components of GPS vectors can be considered either individually (i.e. 1D) or a whole (i.e. 3D). In this study, this consideration is applied for outlier detection process in a simulated GPS vector network and the results are compared with the outlier detection method considering each vector component individually. Results showed that using 3D components for outlier search was more effective when the gross error exists in all components of the baseline whereas the outlier search for 1D component was best conducted when an outlier exists in a component. Findings in this study clearly indicated that both methods should be applied to data sets to detect the outliers so as to ensure the elimination of outliers in the network.     

Low computational-complexity algorithms for vision-aided inertial navigation of micro aerial vehicles

This paper presents low computational-complexity methods for micro-aerial-vehicle localization in GPS-denied environments. All the presented algorithms rely only on the data provided by a single onboard camera and an Inertial Measurement Unit (IMU). This paper deals with outlier rejection and relative-pose estimation. Regarding outlier rejection, we describe two methods. The former only requires the observation of a single feature in the scene and the knowledge of the angular rates from an IMU, under the assumption that the local camera motion lies in a plane perpendicular to the gravity vector. The latter requires the observation of at least two features, but it relaxes the hypothesis on the vehicle motion, being therefore suitable to tackle the outlier detection problem in the case of a 6DoF motion. We show also that if the camera is rigidly attached to the vehicle, motion priors from the IMU can be exploited to discard wrong estimations in the framework of a 2-point-RANSAC-based approach. Thanks to their inherent efficiency, the proposed methods are very suitable for resource-constrained systems. Regarding the pose estimation problem, we introduce a simple algorithm that computes the vehicle pose from the observation of three point features in a single camera image, once that the roll and pitch angles are estimated from IMU measurements. The proposed algorithm is based on the minimization of a cost function. The proposed method is very simple in terms of computational cost and, therefore, very suitable for real-time implementation. All the proposed methods are evaluated on both synthetic and real data.     

Robust probabilistic principal component analysis for process modeling subject to scaled mixture Gaussian noise

Conventionally, for probabilistic principal component analysis (PPCA) based regression models, noise with a Gaussian distribution is assumed for both input and output observations. This assumption makes the model to be vulnerable to large random errors, known as outliers. In this article, unlike the conventional noise assumption, a mixture noise model with a contaminated Gaussian distribution is adopted for probabilistic modeling to diminish the adverse effect of outliers, which usually occur due to irregular process disturbances, instrumentation failures or transmission problems. This is done by downweighing the effect of the noise component which accounts for contamination on output prediction. Outliers are common in process industries; therefore, handling this issue is of practical importance. In comparison with conventional PPCA based regression model, prediction performance of the developed robust probabilistic regression model is improved in presence of data contamination. To evaluate the model performance two case studies were carried out. A simulated set of data with specific characteristics to highlight the presence of outliers was used to demonstrate the robustness of the developed model. The advantages of this robust model are further illustrated via a set of real industrial process data.     

Ensemble Correntropy‐Based Mooney Viscosity Prediction Model for an Industrial Rubber Mixing Process

The development of accurate soft sensors for online prediction of Mooney viscosities in industrial rubber mixing processes is a difficult task because the modeling dataset often contains various outliers. A correntropy kernel learning (CKL) method for robust soft sensor modeling of nonlinear industrial processes with outlier samples is proposed. Simultaneously, the candidate outliers can be identified once the CKL‐based soft sensor model is built. An index for describing the uncertainty of the CKL model is designed. Furthermore, to obtain more robust and accurate predictions, an ensemble CKL (ECKL) method is formulated by introducing the simple bagging strategy. Consequently, by detecting the outliers in a sequential manner, the database becomes more reliable for long‐term use. The application results for the industrial rubber mixing process demonstrate the superiority of ECKL in terms of better prediction performance.     

Fine registration of 3D point clouds fusing structural and photometric information using an RGB-D camera

We address the problem of accurate and efficient alignment of 3D point clouds captured by an RGB-D (Kinect-style) camera from different viewpoints. While the Iterative Closest Point (ICP) algorithm has been widely used for dense point cloud matching, it is limited in its ability to produce accurate results in challenging scenarios involving objects that lack structural features and have significant camera view changes. In this paper, we introduce a new cost function with dynamic weights for the ICP algorithm to tackle this problem. It balances the significance of structural and photometric features with dynamically adjusted weights to improve the error minimization process. Our algorithm also includes a novel outlier rejection method, which adopts adaptive thresholding at each ICP iteration, using both the structural information of the object and the spatial distances of sparse SIFT feature pairs. The effectiveness of our proposed approach is demonstrated by experimental results from various challenging scenarios. We obtained superior registration accuracy than related previous methods, at the same time maintaining low computational requirements.