Time series analysis of wind speed using VAR and the generalized impulse response technique

This research examines the interdependence in time series wind speed data measured in the same location at four different heights. A multiple-equation system known as a vector autoregression is proposed for characterizing the time series dynamics of wind. Additionally, the recently developed method of generalized impulse response analysis provides insight into the cross-effects of the wind series and their responses to shocks. Findings are based on analysis of contemporaneous wind speed time histories taken at 13, 33, 70 and 160 ft above ground level with a sampling rate of 10 Hz. The results indicate that wind speeds measured at 70 ft was the most variable. Further, the turbulence persisted longer at the 70-ft measurement than at the other heights. The greatest interdependence is observed at 13 ft. Gusts at 160 ft led to the greatest persistence to an “own” shock and led to greatest persistence in the responses of the other wind series.     

The privileged sensing framework: A principled approach to improved human-autonomy integration

ABSTRACT

A primary goal for human-autonomy integration (HAI) is to balance the strengths of human and autonomy in order to achieve performance objectives more efficiently and robustly than either the human or autonomous agents would independently. This paper proposes the Privileged Sensing Framework (PSF) as a novel approach to HAI. This approach is based on the concept of dynamically ‘privileging’ information during the process of integration by dynamically bestowing special rights based on the characteristics of each individual agent, the task context, and the performance goals. The proposed framework is tested through a series of simulation experiments that provide a clear demonstration of increased accuracy and throughput of human-autonomy performance. These proof-of-concept simulations provide initial evidence of the utility of the PSF. Continued development of this approach has the potential to revolutionise capabilities of multi-agent cooperative teams across a broad range of applications.     

Information fusion in the immune system

Biologically-inspired methods such as evolutionary algorithms and neural networks are proving useful in the field of information fusion. Artificial immune systems (AISs) are a biologically-inspired approach which take inspiration from the biological immune system. Interestingly, recent research has shown how AISs which use multi-level information sources as input data can be used to build effective algorithms for realtime computer intrusion detection. This research is based on biological information fusion mechanisms used by the human immune system and as such might be of interest to the information fusion community. The aim of this paper is to present a summary of some of the biological information fusion mechanisms seen in the human immune system, and of how these mechanisms have been implemented as AISs.     

Motion teaching method for complex robot links using motor current

Conventional robot motion teaching methods use a teaching pendant or a motion capture device and are not the most convenient or intuitive ways to teach a robot sophisticated and fluid movements such as martial arts motions. Ideally, a robot could be set up as if it were a clothing mannequin that has light limbs and flexible yet frictional joints which can be positioned at desirable shape and hold all the positions. To do the same with a robot, an operator could pull or push the links with minor forces until the desired robot posture is attained. For this, a robot should measure the applied external force by using torque sensors at the robot joints. However, torque sensors are bulky and expensive to install in every DOF joints while keeping a compact design, which is essential to humanoid robots. In this paper, we use only motor current readings to acquire joint torques. The equations used to compensate for the effect of gravity on the joint torques and the self-calibration method to earn link parameters are presented. Additionally, kinematic restrictions can be imposed on the robot’s arms to simplify the motion teaching. Here, we teach the Kendo training robot with this method and the robot’s learnt martial art motions are demonstrated.     

TextonBoost for Image Understanding: Multi-Class Object Recognition and Segmentation by Jointly Modeling Texture,Layout, and Context

This paper details a new approach for learning a discriminative model of object classes, incorporating texture, layout, and context information efficiently. The learned model is used for automatic visual understanding and semantic segmentation of photographs. Our discriminative model exploits texture-layout filters, novel features based on textons, which jointly model patterns of texture and their spatial layout. Unary classification and feature selection is achieved using shared boosting to give an efficient classifier which can be applied to a large number of classes. Accurate image segmentation is achieved by incorporating the unary classifier in a conditional random field, which (i) captures the spatial interactions between class labels of neighboring pixels, and (ii) improves the segmentation of specific object instances. Efficient training of the model on large datasets is achieved by exploiting both random feature selection and piecewise training methods. High classification and segmentation accuracy is demonstrated on four varied databases: (i) the MSRC 21-class database containing photographs of real objects viewed under general lighting conditions, poses and viewpoints, (ii) the 7-class Corel subset and (iii) the 7-class Sowerby database used in He et al. (Proceeding of IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 695–702, June 2004), and (iv) a set of video sequences of television shows. The proposed algorithm gives competitive and visually pleasing results for objects that are highly textured (grass, trees, etc.), highly structured (cars, faces, bicycles, airplanes, etc.), and even articulated (body, cow, etc.). J. Shotton is now working at Toshiba Corporate Research & Development Center, Kawasaki, Japan.     

Attachment, Self-Esteem, Worldviews, and Terror Management: Evidence for a Tripartite Security System.

On the basis of prior work integrating attachment theory and terror management theory, the authors propose a model of a tripartite security system consisting of dynamically interrelated attachment, self-esteem, and worldview processes. Four studies are presented that, combined with existing evidence, support the prediction derived from the model that threats to one component of the security system result in compensatory defensive activation of other components. Further, the authors predicted and found that individual differences in attachment style moderate the defenses. In Studies 1 and 2, attachment threats motivated worldview defense among anxiously attached participants and motivated self-enhancement (especially among avoidant participants), effects similar to those caused by mortality salience. In Studies 3 and 4, a worldview threat and a self-esteem threat caused attachment-related proximity seeking among fearful participants and avoidance of proximity among dismissing participants. The authors' model provides an overarching framework within which to study attachment, self-esteem, and worldviews. (PsycINFO Database Record (c) 2010 APA, all rights reserved)