A nonlinear geometric approach to power system excitation control and stabilization

In this paper, a unified approach to the design of a nonlinear excitation controller/power system stabilizer for a synchronous generator/infinite bus power system is presented. The approach is based on a form of state feedback linearization, known as input–output feedback linearization, which provides an exact semi-global state transformation that is valid for a large class of operating points of the power system. With this transformation, the terminal voltage becomes a linear function of the control input. The excitation controller/power system stabilizer is then synthesized by using linear controller design techniques. The controller is proven to provide small signal stability and to provide local asymptotic tracking of admissible constant reference signals for a large class of operating points. A procedure is given to tune the controller gains to provide significant damping of the power angle oscillations.     

Engineering TCP transmission and retransmission mechanisms for wireless networks

Transmission Control Protocol (TCP) provides mechanisms for reliable data communications. Although it works well in wired networks, it fails to offer satisfactory performance in lossy and wireless environments. And in the multi-hop wireless infrastructure, packet delivery suffers high cumulative loss rate if traveling over multiple wireless hops. The Selective acknowledgment (SACK) is one header option that might be used to combat segment corruptions in air channels. In this paper, an alternative set of flow control mechanisms is proposed to handle high packet loss rate in a wireless medium. Using a measurement-based mechanism, sustainable segment delivery is achievable through a novel size-reduction method. Multiple segment retransmission mechanisms are introduced to reduce successive timeout events. One single byte loss is sufficient to waste all other bytes in a file received at a destination. That is, a good TCP flow control mechanism should provide a high successful file transmission completion rate, and this is set as our design goal. Through thorough simulations, our proposed multi-segment retransmission designs perform with higher successful file transfer rate and fewer timeout events than NewReno and SACK under a wide range of packet loss probabilities.     

Why Teachers Must Learn: Student Innovation as a Driving Factor in the Future of the Web

User innovation is the key to the development and vitality of technology. As Huatong Sun wrote, “expanding the scope of localization practices and linking user localization efforts” to design cycles—including pedagogical design—will help bridge the gap between what teachers/designers create and what skills users/students need and want. This article theorizes the role of modern composition students and teachers as co-constructors of productive spaces for learning critical inquiry based on students’ statuses as digital natives. It focuses on a class of first-year composition students who reshaped an assignment to fit their own needs within the physical classroom and also enacted a shift to a virtual classroom. While doing so, they provided ways for each class member to individualize space within that digital environment, and they focused their project on examining the ways in which social networking forums colonize their daily lives. This article argues that by letting student innovation drive pedagogical practice—just as social media creators let user innovation drive the digital structures they produce—composition teachers can be assured of having a text for critique that blurs the lines between “private” student underlife and “public” classroom practice and legitimizes the creation of student-produced learning spaces. Using critical and cyberfeminist theories as lenses, this article draws conclusions about the future of computer usage in composition pedagogy based on students’ abilities to re-appropriate physical and digital classroom space.     

Distributed model predictive control of dynamically decoupled systems with coupled cost

This paper considers the distributed model predictive control (DMPC) of systems with interacting subsystems having decoupled dynamics and constraints but coupled costs. An easily-verifiable constraint is introduced to ensure asymptotic stability of the overall system in the absence of disturbance. The constraint introduced has a parameter which allows for the performance of the DMPC system to approach that controlled by a centralized model predictive controller. When the subsystems are linear and additive disturbance is present, the added constraint ensures the state of each subsystem converges to its respective minimal disturbance invariant set. The approach is demonstrated via several numerical examples.     

Design reuse oriented partial retrieval of CAD models

As a huge number of 3D CAD models is generated each year, retrieval of 3D CAD models is becoming more and more important for achieving design reuse. However, the existing methods for partial retrieval of 3D CAD models are very few and far from the requirements of design reuse. In this paper, we present an approach to partial retrieval of 3D CAD models for design reuse. The criteria for determining whether a subpart of 3D CAD models is reusable for design is defined first. Based on the criteria defined, all the design reusable subparts involved in the 3D CAD models in the library are automatically extracted and stored in the library as reference models. Moreover, each design reusable subpart in the library is represented by all its local matching regions in a hierarchical way so as to support multi-mode partial retrieval. In our approach, three partial retrieval modes including normal retrieval, exact retrieval and relaxed retrieval are defined to meet various partial retrieval requirements of design reuse such as the incomplete and vague queries during the early design stage. And the multi-mode partial retrieval is achieved by performing multi-mode matching and similarity assessment between the query and the design reusable subparts in the library indexed by bitmap. Experimental results are presented to demonstrate the effectiveness of the approach.     

A dynamic priority-based approach to concurrent toolpath planning for multi-material layered manufacturing

This paper presents an approach to concurrent toolpath planning for multi-material layered manufacturing (MMLM) to improve the fabrication efficiency of relatively complex prototypes. The approach is based on decoupled motion planning for multiple moving objects, in which the toolpaths of a set of tools are independently planned and then coordinated to deposit materials concurrently. Relative tool positions are monitored and potential tool collisions detected at a predefined rate. When a potential collision between a pair of tools is detected, a dynamic priority scheme is applied to assign motion priorities of tools. The traverse speeds of tools along the x-axis are compared, and a higher priority is assigned to the tool at a higher traverse speed. A tool with a higher priority continues to deposit material along its original path, while the one with a lower priority gives way by pausing at a suitable point until the potential collision is eliminated. Moreover, the deposition speeds of tools can be adjusted to suit different material properties and fabrication requirements. The proposed approach has been incorporated in a multi-material virtual prototyping (MMVP) system. Digital fabrication of prototypes shows that it can substantially shorten the fabrication time of relatively complex multi-material objects. The approach can be adapted for process control of MMLM when appropriate hardware becomes available. It is expected to benefit various applications, such as advanced product manufacturing and biomedical fabrication.     

Semantic modeling of natural scenes based on contextual Bayesian networks

This paper presents a novel approach based on contextual Bayesian networks (CBN) for natural scene modeling and classification. The structure of the CBN is derived based on domain knowledge, and parameters are learned from training images. For test images, the hybrid streams of semantic features of image content and spatial information are piped into the CBN-based inference engine, which is capable of incorporating domain knowledge as well as dealing with a number of input evidences, producing the category labels of the entire image. We demonstrate the promise of this approach for natural scene classification, comparing it with several state-of-art approaches.     

LPP solution schemes for use with face recognition

Locality preserving projection (LPP) is a manifold learning method widely used in pattern recognition and computer vision. The face recognition application of LPP is known to suffer from a number of problems including the small sample size (SSS) problem, the fact that it might produce statistically identical transform results for neighboring samples, and that its classification performance seems to be heavily influenced by its parameters. In this paper, we propose three novel solution schemes for LPP. Experimental results also show that the proposed LPP solution scheme is able to classify much more accurately than conventional LPP and to obtain a classification performance that is only little influenced by the definition of neighbor samples.     

Inter-image outliers and their application to image classification

Image variability that is impossible or difficult to restore by intra-image processing, such as the variability caused by occlusions, significantly reduces the performance of image-recognition methods. To address this issue, we propose that the pixels associated with large distances obtained by inter-image pixel-by-pixels comparisons should be considered as inter-image outliers and should be removed from the similarity calculation used for the image classification. When this method is combined with the template-matching method for image recognition, it leads to state-of-the-art recognition performance: 91% with AR database that includes occluded face images, 90% with PUT database that includes pose variations of face images and 100% with EYale B database that includes images with large illumination variation.     

Clustering of temporal gene expression data by regularized spline regression and an energy based similarity measure

Clustering analysis of temporal gene expression data is widely used to study dynamic biological systems, such as identifying sets of genes that are regulated by the same mechanism. However, most temporal gene expression data often contain noise, missing data points, and non-uniformly sampled time points, which imposes challenges for traditional clustering methods of extracting meaningful information. In this paper, we introduce an improved clustering approach based on the regularized spline regression and an energy based similarity measure. The proposed approach models each gene expression profile as a B-spline expansion, for which the spline coefficients are estimated by regularized least squares scheme on the observed data. To compensate the inadequate information from noisy and short gene expression data, we use its correlated genes as the test set to choose the optimal number of basis and the regularization parameter. We show that this treatment can help to avoid over-fitting. After fitting the continuous representations of gene expression profiles, we use an energy based similarity measure for clustering. The energy based measure can include the temporal information and relative changes of the time series using the first and second derivatives of the time series. We demonstrate that our method is robust to noise and can produce meaningful clustering results.