Cross-person activity recognition using reduced kernel extreme learning machine

Activity recognition based on mobile embedded accelerometer is very important for developing human-centric pervasive applications such as healthcare, personalized recommendation and so on. However, the distribution of accelerometer data is heavily affected by varying users. The performance will degrade when the model trained on one person is used to others. To solve this problem, we propose a fast and accurate cross-person activity recognition model, known as TransRKELM (Transfer learning Reduced Kernel Extreme Learning Machine) which uses RKELM (Reduced Kernel Extreme Learning Machine) to realize initial activity recognition model. In the online phase OS-RKELM (Online Sequential Reduced Kernel Extreme Learning Machine) is applied to update the initial model and adapt the recognition model to new device users based on recognition results with high confidence level efficiently. Experimental results show that, the proposed model can adapt the classifier to new device users quickly and obtain good recognition performance.     

基于微粒群优化算法和支持向量机的软测量建模

在分析基本微粒群优化算法(PSO)和支持向量机(SVM)原理的基础上,采用带有末位淘汰机制的微粒群优化算法优化支持向量机的参数,建立了延迟焦化装置粗汽油干点软测量的微粒群支持向量机模型.该方法利用支持向量机结构风险最小化原则和PSO算法快速全局优化的特点,用于软测量建模.仿真实验表明:所建模型的泛化性能较好,模型具有较高的精度.     

Analyzing infant head flatness and asymmetry using kernel density estimation of directional surface data from a craniofacial 3D model

Infant skull deformation is analyzed using the distribution of head normal vector directions computed from a 3D image. Severity of flatness and asymmetry are quantified by functionals of the kernel estimate of the normal vector direction density. Using image data from 99 infants and clinical deformation ratings made by experts, our approach is compared with some recently suggested methods. The results show that the proposed method performs competitively. Copyright © 2016 John Wiley & Sons, Ltd.     

When can it be not optimal to adopt a new technology? A viability theory solution to a two‐stage optimal control problem of new technology adoption

Politicians often deplore economic agents’ behaviour when they do not accept new technologies. For a new technology to be adopted, the new technology value function needs to dominate the old technology value function. If this is the case, a technology switch will occur. We characterise the value functions, without computing them, using the fact that their hypographs are viability kernels of some auxiliary control problems and study whether the graphs intersect. If they do not, the corresponding value functions do not dominate each other, and the switch cannot occur at a positive time. Using this characterisation, we analyse a technology adoption problem and show how to recognise the models, for which the switch will occur at time zero or never, without solving an optimal control problem. We conclude that the current control regime may not change if the economic agents’ preferences are modelled as an integral of discounted differences between a reward from the flow variable (control) and a penalty from the stock variable (state).Copyright © 2011 John Wiley & Sons, Ltd.     

Solving chance constrained optimal control problems in aerospace via kernel density estimation

The goal of this paper is to show how nonparametric statistics can be used to solve some chance constrained optimization and optimal control problems. We use the kernel density estimation method to approximate the probability density function of a random variable with unknown distribution from a relatively small sample. We then show how this technique can be applied and implemented for a class of problems including the Goddard problem and the trajectory optimization of an Ariane five‐like launcher.     

Nonparametric analysis of dependently interval‐censored failure time data

Failure time studies based on observational cohorts often have to deal with irregular intermittent observation of individuals, which produces interval‐censored failure times. When the observation times depend on factors related to a person's failure time, the failure times may be dependently interval censored. Inverse‐intensity‐of‐visit weighting methods have been developed for irregularly observed longitudinal or repeated measures data and recently extended to parametric failure time analysis. This article develops nonparametric estimation of failure time distributions using weighted generalized estimating equations and monotone smoothing techniques. Simulations are conducted for examination of the finite sample performance of proposed estimators. This research is motivated in part by the Toronto Psoriatic Arthritis Cohort Study, and the proposed methodology is applied to this study.     

柱前衍生化HPLC法测定薏苡非种仁部位中丙氨酸的含量

目的:建立柱前衍生化反相高效液相色谱法测定薏苡非种仁部位中氨基酸含量的方法。方法:采用Shim-pack CLC-ODS(4.6 mm)色谱柱,流动相A为0.1 mol/L乙酸钠溶液(pH=6.5)-乙腈(93∶7),流动相B为乙腈-水(4∶1),梯度洗脱,柱温为40℃,检测波长为254 nm。结果:丙氨酸进样量在0.01296～0.2851μg范围内,线性关系良好,r=0.9998(n=7),平均加样回收率为98.01%,RSD为2.2%。结论:方法准确、可靠,具有良好的重复性和稳定性,可用于薏苡非种仁部位中氨基酸的含量测定。     

Bayesian semiparametric regression models for evaluating pathway effects on continuous and binary clinical outcomes

Many statistical methods for microarray data analysis consider one gene at a time, and they may miss subtle changes at the single gene level. This limitation may be overcome by considering a set of genes simultaneously where the gene sets are derived from prior biological knowledge. Limited work has been carried out in the regression setting to study the effects of clinical covariates and expression levels of genes in a pathway either on a continuous or on a binary clinical outcome. Hence, we propose a Bayesian approach for identifying pathways related to both types of outcomes. We compare our Bayesian approaches with a likelihood‐based approach that was developed by relating a least squares kernel machine for nonparametric pathway effect with a restricted maximum likelihood for variance components. Unlike the likelihood‐based approach, the Bayesian approach allows us to directly estimate all parameters and pathway effects. It can incorporate prior knowledge into Bayesian hierarchical model formulation and makes inference by using the posterior samples without asymptotic theory. We consider several kernels (Gaussian, polynomial, and neural network kernels) to characterize gene expression effects in a pathway on clinical outcomes. Our simulation results suggest that the Bayesian approach has more accurate coverage probability than the likelihood‐based approach, and this is especially so when the sample size is small compared with the number of genes being studied in a pathway. We demonstrate the usefulness of our approaches through its applications to a type II diabetes mellitus data set. Our approaches can also be applied to other settings where a large number of strongly correlated predictors are present. Copyright © 2012 John Wiley & Sons, Ltd.     

Kernel Machine SNP‐Set Testing Under Multiple Candidate Kernels

Joint testing for the cumulative effect of multiple single‐nucleotide polymorphisms grouped on the basis of prior biological knowledge has become a popular and powerful strategy for the analysis of large‐scale genetic association studies. The kernel machine (KM)‐testing framework is a useful approach that has been proposed for testing associations between multiple genetic variants and many different types of complex traits by comparing pairwise similarity in phenotype between subjects to pairwise similarity in genotype, with similarity in genotype defined via a kernel function. An advantage of the KM framework is its flexibility: choosing different kernel functions allows for different assumptions concerning the underlying model and can allow for improved power. In practice, it is difficult to know which kernel to use a priori because this depends on the unknown underlying trait architecture and selecting the kernel which gives the lowest P‐value can lead to inflated type I error. Therefore, we propose practical strategies for KM testing when multiple candidate kernels are present based on constructing composite kernels and based on efficient perturbation procedures. We demonstrate through simulations and real data applications that the procedures protect the type I error rate and can lead to substantially improved power over poor choices of kernels and only modest differences in power vs. using the best candidate kernel.