Bayesian bivariate generalized Lindley model for survival data with a cure fraction

The cure fraction models have been widely used to analyze survival data in which a proportion of the individuals is not susceptible to the event of interest. In this article, we introduce a bivariate model for survival data with a cure fraction based on the three-parameter generalized Lindley distribution. The joint distribution of the survival times is obtained by using copula functions. We consider three types of copula function models, the Farlie–Gumbel–Morgenstern (FGM), Clayton and Gumbel–Barnett copulas. The model is implemented under a Bayesian framework, where the parameter estimation is based on Markov Chain Monte Carlo (MCMC) techniques. To illustrate the utility of the model, we consider an application to a real data set related to an invasive cervical cancer study.     

Fall detection for multiple pedestrians using depth image processing technique

A fall detection method based on depth image analysis is proposed in this paper. As different from the conventional methods, if the pedestrians are partially overlapped or partially occluded, the proposed method is still able to detect fall events and has the following advantages: (1) single or multiple pedestrian detection; (2) recognition of human and non-human objects; (3) compensation for illumination, which is applicable in scenarios using indoor light sources of different colors; (4) using the central line of a human silhouette to obtain the pedestrian tilt angle; and (5) avoiding misrecognition of a squat or stoop as a fall. According to the experimental results, the precision of the proposed fall detection method is 94.31% and the recall is 85.57%. The proposed method is verified to be robust and specifically suitable for applying in family homes, corridors and other public places.     

异常高压气井生产动态仿真研究及应用

异常高压气井生产动态仿真方法不仅改变了传统物理仿真成本高、过程复杂的缺点,而且具备了计算机仿真模拟的准确性。考虑异常高压气井流体高压、高温的特点,本文将异常高压气井生产压降分解为地层流动压降、井筒流动压降和嘴流流动压降三个过程,并建立了气井生产动态流动数学模型;本文利用计算机仿真技术模拟了异常高压气井地层流体流动过程、井筒流体流动过程和气嘴流体流动过程,并预测了气井生产动态流动参数,为下步异常高压气井动态配产提供了基础数据。通过实例分析,将计算机仿真预测出的异常高压气井流动参数代入优化配产模型计算出配产结果与实际配产结果十分接近,表明异常高压气井生产动态仿真对实际生产具有重要的指导意义。     

Spline-based meshfree method with extended basis

In this work, an extension has been performed on the analysis basis of spline-based meshfree method (SBMFM) to stabilize its solution. The potential weakness of the SBMFM is its numerical instability from using regular grid background mesh. That is, if an extremely small trimmed element is produced by the trimming curves that represent boundaries of the analysis domain, it can induce an excessively large condition number in global system matrix. To resolve the instability problem, the extension technique of the weighted extended B-spline (WEB-spline) is implemented in the SBMFM. The basis functions with very small trimmed supports are extrapolated by neighboring basis functions with some special scheme so that those basis functions can be condensed in the solution process. In order to impose essential boundary conditions in the SBMFM with extended basis, Nitsche's method is implemented. Using numerical examples, the presented SBMFM with extended basis is shown to be valid and effective. Moreover, the condition number of the system is well-managed guaranteeing the stability of the numerical analysis.     

Time-series primitive static states for detailing work state and flow of human-operated work machine

This paper proposes a quantification method for a comprehensive work flow in construction work for describing work states in more detail on the basis of analyzing state transitions of primitive static states (PSS), which consist of 16 symbolic work states defined by using on-off state of the lever operations and joint loads for the manipulator and end-effector. On the basis of the state transition rules derived from a transition-condition analysis, practical state transitions (PST), which are common and frequent transitions in arbitrary construction work, are defined. PST can be classified into essential state transition (EST) or nonessential state transitions (NST). EST extracts common phases of work progress and estimates positional relations between a manipulator and an object. NST reveals wasted movements that degrade the efficiency and quality of work. To evaluate comprehensive work flows modeled by combining EST and NST, work-analysis experiments using our instrumented setup were conducted. Results indicate that all the PSS definitely changes on the basis of PST under various work conditions, and work analysis using EST and NST easily reveals work characteristics and untrained tasks related to wasted movements.     

Noncertainty equivalent adaptive control of robot manipulators without velocity measurements

This paper presents a noncertainty equivalent adaptive motion control scheme for robot manipulators in the absence of link velocity measurements. A new output feedback adaptation algorithm, based on the attractive manifold design approach, is developed. A proportional-integral adaptation is selected for the adaptive parameter estimator to strengthen the passivity of the system. In order to relieve velocity measurements, an observer is designed to estimate the velocities. The controller guarantees semiglobal asymptotic motion tracking and velocity estimation, as well as L_∞ and L₂ bounded parameter estimation error. The effectiveness of the proposed controller is verified by simulations for a two-link robot manipulator and a four-bar linkage. The results are further compared with the earlier certainty-equivalent adaptive partial and full state feedback controller to highlight potential closed-loop performance improvements.     

Delay time compensation based on coefficient of restitution for collision hybrid motion simulator

A hybrid motion simulator embeds a hardware experiment in a numerical simulation loop. However, it is often subjected to the inherent problem of an energy increase in the collision of two pieces of hardware in a loop because of the delay time. This paper proposes a delay time compensation method based on contact dynamics model for a collision hybrid motion simulator under delay time and establishes a compensation method for coupled translational and rotational motion. The model developed in this paper describes linear uniform motion of a floating object during the period of the delay time until the force and torque are observed and non-linear motion according to environmental stiffness after the initial delay time period in contact. By using the above model, compensation parameters are designed based on desired coefficient of restitution with iterative calculation. The proposed method achieves accurate delay time compensation and simultaneously realizes a variable desired coefficient of restitution over a wide range of frequencies. Furthermore, the compensation method for multi-dimensional motion is established under the assumption that the friction effect is very small. The efficiency of the proposed method is verified through collision experiments for the coupled motion in two dimensions.     

一次风机节能改造

为解决一次风机出口风压低、火焰无力度、系统功耗高等问题,采用空气悬浮风机对其进行节能改造。拆除原有的一次风机备用风机,在原有底座上安装空气悬浮风机,整体设计安装悬浮风机出口管道。改造后,一次风机出口风压明显增大,煤粉燃烧充分,火焰调整灵活,每小时可节电30kWh,两处对空排放管道加装消声器,降噪效果显著。     

An ExPosition of multivariate analysis with the singular value decomposition in R

ExPosition is a new comprehensive R package providing crisp graphics and implementing multivariate analysis methods based on the singular value decomposition (svd). The core techniques implemented in ExPosition are: principal components analysis, (metric) multidimensional scaling, correspondence analysis, and several of their recent extensions such as barycentric discriminant analyses (e.g., discriminant correspondence analysis), multi-table analyses (e.g.,multiple factor analysis, Statis, and distatis), and non-parametric resampling techniques (e.g., permutation and bootstrap). Several examples highlight the major differences between ExPosition and similar packages. Finally, the future directions of ExPosition are discussed.     

Online learning and fusion of orientation appearance models for robust rigid object tracking

We introduce a robust framework for learning and fusing of orientation appearance models based on both texture and depth information for rigid object tracking. Our framework fuses data obtained from a standard visual camera and dense depth maps obtained by low-cost consumer depth cameras such as the Kinect. To combine these two completely different modalities, we propose to use features that do not depend on the data representation: angles. More specifically, our framework combines image gradient orientations as extracted from intensity images with the directions of surface normals computed from dense depth fields. We propose to capture the correlations between the obtained orientation appearance models using a fusion approach motivated by the original Active Appearance Models (AAMs). To incorporate these features in a learning framework, we use a robust kernel based on the Euler representation of angles which does not require off-line training, and can be efficiently implemented online. The robustness of learning from orientation appearance models is presented both theoretically and experimentally in this work. This kernel enables us to cope with gross measurement errors, missing data as well as other typical problems such as illumination changes and occlusions. By combining the proposed models with a particle filter, the proposed framework was used for performing 2D plus 3D rigid object tracking, achieving robust performance in very difficult tracking scenarios including extreme pose variations.