Linear,parameter-varying control of a supercavitating vehicle

A systematic approach to parameter-dependent control synthesis of a high-speed supercavitation vehicle (HSSV) is presented. The aim of the control design is to provide robust reference tracking across a large flight envelope, while directly accounting for the interaction of liquid and gas phases with the vehicle. A nonlinear dynamic HSSV model is presented and discussed relative to the actual vehicle. A linear, parameter-varying (LPV) controller is synthesized for angle rate tracking in the presence of model uncertainty. The control design takes advantage of coupling in the governing equations to achieve improved performance. Multiple LPV controllers synthesized for smaller overlapping regions of the parameter space are blended together, providing a single controller for the full flight envelope. Time-domain simulations implemented on high-fidelity simulations, provide insight into the performance and robustness of the proposed scheme.     

Optimization of silicon solar cell fabrication based on neural network and genetic programming modeling

This study describes techniques for the cascade modeling and the optimization that are required to conduct the simulator-based process optimization of solar cell fabrication. Two modeling approaches, neural networks and genetic programming, are employed to model the crucial relation for the consecutively connected two processes in solar cell fabrication. One model (Model 1) is used to map the five inputs (time, amount of nitrogen and DI water in surface texturing and temperature and time in emitter diffusion) to the two outputs (reflectance and sheet resistance) of the first process. The other model (Model 2) is used to connect the two inputs (reflectance and sheet resistance) to the one output (efficiency) of the second process. After modeling of the two processes, genetic algorithms and particle swarm optimization were applied to search for the optimal recipe. In the first optimization stage, we searched for the optimal reflectance and sheet resistance that can provide the best efficiency in the fabrication process. The optimized reflectance and sheet resistance found by the particle swarm optimization were better than those found by the genetic algorithm. In the second optimization stage, the five input parameters were searched by using the reflectance and sheet resistance values obtained in the first stage. The found five variables such as the texturing time, amount of nitrogen, DI water, diffusion time, and temperature are used as a recipe for the solar cell fabrication. The amount of nitrogen, DI water, and diffusion time in the optimized recipes showed considerable differences according to the modeling approaches. More importantly, repeated applications of particle swarm optimization yielded process conditions with smaller variations, implying greater consistency in recipe generation.     

<Emphasis Type="Italic">A Midsummer Night’s Dream</Emphasis> (with flying robots)

Seven flying robot “fairies” joined human actors in the Texas A&;M production of William Shakespeare’s A Midsummer Night’s Dream. The production was a collaboration between the departments of Computer Science and Engineering, Electrical and Computer Engineering, and Theater Arts. The collaboration was motivated by two assertions. First, that the performing arts have principles for creating believable agents that will transfer to robots. Second, the theater is a natural testbed for evaluating the response of untrained human groups (both actors and the audience) to robots interacting with humans in shared spaces, i.e., were believable agents created? The production used two types of unmanned aerial vehicles, an AirRobot 100-b quadrotor platform about the size of a large pizza pan, and six E-flite Blade MCX palm-sized toy helicopters. The robots were used as alter egos for fairies in the play; the robots did not replace any actors, instead they were paired with them. The insertion of robots into the production was not widely advertised so the audience was the typical theatergoing demographic, not one consisting of people solely interested technology. The use of radio-controlled unmanned aerial vehicles provides insights into what types of autonomy are needed to create appropriate affective interactions with untrained human groups. The observations from the four weeks of practice and eight performances contribute (1) a taxonomy and methods for creating affect exchanges between robots and untrained human groups, (2) the importance of improvisation within robot theater, (3) insights into how untrained human groups form expectations about robots, and (4) awareness of the importance of safety and reliability as a design constraint for public engagement with robot platforms. The taxonomy captures that apparent affect can be created without explicit affective behaviors by the robot, but requires talented actors to convey the situation or express reactions. The audience’s response to robot crashes was a function of whether they had the opportunity to observe how the actors reacted to robot crashes on stage, suggesting that pre-existing expectations must be taken into account in the design of autonomy. Furthermore, it appears that the public expect robots to be more reliable (an expectation of consumer product hardening) and safe (an expectation from product liability) than the current capabilities and this may be a major challenge or even legal barrier for introducing robots into shared public spaces. These contributions are expected to inform design strategies for increasing public engagement with robot platforms through affect, and shows the value of arts-based approaches to public encounters with robots both for generating design strategies and for evaluation.     

Iteratively constrained selection of word alignment links using knowledge and statistics

Word alignment is a crucial component in applications that use bilingual resources. Statistical methods are widely used because they are portable and allow simple system building. However, pure statistical methods often incorrectly align functional words in the English–Korean language pair due to differences in the typology of the languages and a lack of knowledge. Knowledge is inevitably required to correct errors and to improve word alignment quality. In this paper, we introduce an effective method that uses an iterative process to incorporate knowledge into the word alignment system. The method achieved significant improvements in word alignment and its application: statistical machine translation.     

Deriving industrial logistics hub reference models for manufacturing based economies

Global enterprises require extended logistics operations that integrate channel intermediaries into the network. The method for deriving integrated models for logistics hubs that improve the efficiency of manufacturers’ global operations is the focus of this paper. Under government sponsorship, six industrial logistics hubs were implemented by leading manufacturing sectors across Taiwan over a five year period from 2004 until 2008. Each industry sector had unique industry characteristics, business strategies, and logistics models. Therefore, this paper describes how generalized and quick to implement integrated logistics hubs are developed by studying the successful reference models and systems used by six independent industrial sectors. The research results provide a field tested method for deriving integrated logistics hub models in different manufacturing economies with notes that provide sufficient methodological detail for repeating the construction of logistics hubs in other manufacturing economies.     

Multimodal augmented reality tangible gaming

This paper presents tangible augmented reality gaming environment that can be used to enhance entertainment using a multimodal tracking interface. Players can interact using different combinations between a pinch glove, a Wiimote, a six-degrees-of-freedom tracker, through tangible ways as well as through I/O controls. Two tabletop augmented reality games have been designed and implemented including a racing game and a pile game. The goal of the augmented reality racing game is to start the car and move around the track without colliding with either the wall or the objects that exist in the gaming arena. Initial evaluation results showed that multimodal-based interaction games can be beneficial in gaming. Based on these results, an augmented reality pile game was implemented with goal of completing a circuit of pipes (from a starting point to an end point on a grid). Initial evaluation showed that tangible interaction is preferred to keyboard interaction and that tangible games are much more enjoyable.     

A computer-enabled solution procedure for food wholesalers’ distribution decision in cities with a circular transportation infrastructure

This paper addresses the food distribution decision from a wholesaler's perspective, as the wholesalers are often faced with thorny issues such as when, where, how to deliver products at the lowest costs to best satisfy the retailers’ changing needs. This research relies on a distribution problem encountered at Northern Grocery Company in Beijing as an example to develop a two-stage solution procedure and subsequent computer-enabled programs for identifying efficient vehicle routing alternatives. The solution process, which integrates heuristic search algorithms and an integer programming model, can be applied to a wide range of distribution problems with specified route durations and a circular transportation network structure as exemplified by City of Beijing.     

Failure mode and effects analysis using a group-based evidential reasoning approach

Failure mode and effects analysis (FMEA) is a methodology to evaluate a system, design, process or service for possible ways in which failures (problems, errors, risks and concerns) can occur. It is a group decision function and cannot be done on an individual basis. The FMEA team often demonstrates different opinions and knowledge from one team member to another and produces different types of assessment information such as complete and incomplete, precise and imprecise and known and unknown because of its cross-functional and multidisciplinary nature. These different types of information are very difficult to incorporate into the FMEA by the traditional risk priority number (RPN) model and fuzzy rule-based approximate reasoning methodologies. In this paper we present an FMEA using the evidential reasoning (ER) approach, a newly developed methodology for multiple attribute decision analysis. The proposed FMEA is then illustrated with an application to a fishing vessel. As is illustrated by the numerical example, the proposed FMEA can well capture FMEA team members’ diversity opinions and prioritize failure modes under different types of uncertainties.     

Google Wave: Have CTSA-Minded Institutions Caught It?

BACKGROUND: Google Wave was touted as the next big communication tool-combining e-mail, social networking, and chat within a single "wave"-with the potential to create a new world for collaboration. Information professionals who are knowledgeable of this tool and its capabilities could become uniquely situated to use it, evaluate it, and teach it. This seemed especially true for those working within Clinical and Translational Science Award (CTSA)-minded institutions, given the promise of interdisciplinary collaboration between investigators and the potential for creating new authorship models. This case study on Google Wave users who are affiliated with CTSA-minded institutions, was designed for and presented at the Evidence-Based Scholarly Communication Conference held by the University of New Mexico Health Sciences Library and Information Center. It provides an early evidence based evaluation of Google Wave's potential. METHODS: Two "waves" were created. The first consisted of five survey questions designed to collect demographic data on the respondents' roles, a general impression of Wave, the specific tools within Wave that might be useful, and potential collaborators with whom the respondents might use Wave. The second wave was a private, guided discussion on Wave's collaboration potential. Individuals from CTSA-minded institutions were invited to participate with messages on Twitter, forums, blogs, and electronic mail lists, although there were difficulties reaching out to these institutions as a group. RESULTS: By the conclusion of the study, only a small number of people (n=11, with a viable n=9) had responded to the survey. Given this small result set, it made sense to group the responses by the respondents' roles (CTSA staff and researchers, support staff, medical librarian, or general public) and to treat them as individual cases. Most of the respondents were librarians and support staff who felt that Wave might have potential for collaboration; there were no CTSA researcher respondents. For the second part of the study, the discussion wave, only one participant explicitly expressed interest in joining. All were invited to join, but there was no participation in the discussion wave at the conclusion of the study. CONCLUSIONS: The results of this study implied that Google Wave was not on the forefront of CTSA-minded institutions' communication strategies. However, it was being used, and it did demonstrate new collaboration and authorship capabilities. Being generally aware of these capabilities may be useful to information professionals who seek to be current and informed regarding developing technology and to those interested in scholarly communication practices. In addition, the difficulties encountered during this case study in attempting to reach out to CTSA-minded institutions raised the question of how members currently communicate with each other as institutions and as individuals. There was a lesson learned in the usefulness of doing case-study research to evaluate new technologies; the cost in terms of time was relatively low, and knowledge about the technology itself was gained while establishing a base level of evidence to potentially build on in the future.     

Integration of MODIS-derived metrics to assess interannual variability in snowpack, lake ice, and NDVI in southwest Alaska

Impacts of global climate change are expected to result in greater variation in the seasonality of snowpack, lake ice, and vegetation dynamics in southwest Alaska. All have wide-reaching physical and biological ecosystem effects in the region. We used Moderate Resolution Imaging Spectroradiometer (MODIS) calibrated radiance, snow cover extent, and vegetation index products for interpreting interannual variation in the duration and extent of snowpack, lake ice, and vegetation dynamics for southwest Alaska. The approach integrates multiple seasonal metrics across large ecological regions.Throughout the observation period (2001-2007), snow cover duration was stable within ecoregions, with variable start and end dates. The start of the lake ice season lagged the snow season by 2 to 3 months. Within a given lake, freeze-up dates varied in timing and duration, while break-up dates were more consistent. Vegetation phenology varied less than snow and ice metrics, with start-of-season dates comparatively consistent across years. The start of growing season and snow melt were related to one another as they are both temperature dependent. Higher than average temperatures during the El Niño winter of 2002-2003 were expressed in anomalous ice and snow season patterns. We are developing a consistent, MODIS-based dataset that will be used to monitor temporal trends of each of these seasonal metrics and to map areas of change for the study area.