Pursuit Evasion: The Herding Non-cooperative Dynamic Game

A class of pursuit evasion problems is studied. This problem involves a dog agent herding a sheep agent in order to take the sheep to a pen. The problem is stated in terms of the allowable sequential actions of the two agents and the game being played because of the choices each agent has. The solution is obtained using the dynamic programming principle applied in the game setting. The algorithm is analyzed and simulation results are presented

Neural network identification and control of a parametrically excited structural dynamic model of an F-15 tail section,

We investigated the design of a neural-network-based adaptive control system for a smart structural dynamic model of the twin tails of an F-15 tail section. A neural network controller was developed and tested in computer simulation for active vibration suppression of the model subjected to parametric excitation. First, an emulator neural network was trained to represent the structure to be controlled and thus used in predicting the future responses of the model. Second, a neurocontroller to determine the necessary control action on the structure was developed. The control was implemented through the application of a smart material actuator. A strain gauge sensor was assumed to be on each tail. Results from computer-simulation studies have shown great promise for control of the vibration of the twin tails under parametric excitation using artificial neural networks

Magnetic White Dwarfs from the SDSS II. The Second and Third Data Releases

Fifty-two magnetic white dwarfs have been identified in spectroscopic observations from the Sloan Digital Sky Survey (SDSS) obtained between mid-2002 and the end of 2004, including Data Releases 2 and 3. Though not as numerous nor as diverse as the discoveries from the first Data Release, the collection exhibits polar field strengths ranging from 1.5MG to ~1000MG, and includes two new unusual atomic DQA examples, a molecular DQ, and five stars that show hydrogen in fields above 500MG. The highest-field example, SDSSJ2346+3853, may be the most strongly magnetic white dwarf yet discovered. Analysis of the photometric data indicates that the magnetic sample spans the same temperature range as for nonmagnetic white dwarfs from the SDSS, and support is found for previous claims that magnetic white dwarfs tend to have larger masses than their nonmagnetic counterparts. A glaring exception to this trend is the apparently low-gravity object SDSSJ0933+1022, which may have a history involving a close binary companion.Comment: 20 pages, 4 figures Accepted for publication in the Astronomical Journa

Sequencing of Culex quinquefasciatus establishes a platform for mosquito comparative genomics

Culex quinquefasciatus (the southern house mosquito) is an important mosquito vector of viruses such as West Nile virus and St. Louis encephalitis virus, as well as of nematodes that cause lymphatic filariasis. C. quinquefasciatus is one species within the Culex pipiens species complex and can be found throughout tropical and temperate climates of the world. The ability of C. quinquefasciatus to take blood meals from birds, livestock, and humans contributes to its ability to vector pathogens between species. Here, we describe the genomic sequence of C. quinquefasciatus: Its repertoire of 18,883 protein-coding genes is 22% larger than that of Aedes aegypti and 52% larger than that of Anopheles gambiae with multiple gene-family expansions, including olfactory and gustatory receptors, salivary gland genes, and genes associated with xenobiotic detoxification

Pseudo-linear identification: Optimal joint parameter and state estimation of linear stochastic MIMO systems TP2 -3:30 PSEUDO-LINEAR IDENTIFICATION: OPTIMAL JOINT PARAMETER AND STATE ESTIMATION OF LINEAR STOCHASTIC MIMO SYSTEMS

ABSTRAC

On Fusion of Soft and Hard Computing: Traditional ( Hard Computing ) Optimal Rescaling Techniques Simplify Fuzzy Control

One of the main objectives of fuzzy control is to translate expert rules - formulated in imprecise ( fuzzy ) words from natural language - into a precise control strategy. This translation is usually done is two steps. First, we apply a fuzzy control methodology to get a rough approximation to the expert\u27s control strategy, and then we tune the resulting fuzzy control system. The first step (getting a rough approximation) is well-analyzed, and the fact that we have expert\u27s intuitive understanding enables us to use soft computing techniques to perform this step. The second (tuning) step is much more difficult: we no longer have any expert understanding of which tuning is better, and therefore, soft computing techniques are not that helpful. In this paper, we show that we can formulate an important particular case of the tuning problem as a traditional optimization problem and solve it by using traditional ( hard computing ) techniques. We show, on a practical industrial control example, that the resulting fusion of soft computing (for a rough approximation) and a hard computing (for tuning) leads to a high quality control

On Fusion of Soft and Hard Computing: Traditional ("Hard Computing") Optimal Rescaling Techniques Simplify Fuzzy Control

One of the main objectives of fuzzy control is to translate expert rules -- formulated in imprecise ("fuzzy") words from natural language -- into a precise control strategy. This translation is usually done is two steps. First, we apply a fuzzy control methodology to get a rough approximation to the expert's control strategy, and then we tune the resulting fuzzy control system. The first step (getting a rough approximation) is well-analyzed, and the fact that we have expert's intuitive understanding enables us to use soft computing techniques to perform this step. The second (tuning) step is much more difficult: we no longer have any expert understanding of which tuning is better, and therefore, soft computing techniques are not that helpful. In this paper, we show that we can formulate an important particular case of the tuning problem as a traditional optimization problem and solve it by using traditional ("hard computing ") techniques. We show, on a practical industrial control exa..