Control method of 4WS based on neural network

This paper introduces an active 4WS control method based on neural network. The method takes nonlinear dynamic characteristic of vehicle and tire into account. And discerns them by neural network method according to those actual survey data come from real vehicle. It shows that it has a good control property and can improve the safety and handling stability of vehicle effectively. © 2012 TSI Press

Altered functional connectivity in persistent developmental stuttering

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Weighted entropy and optimal portfolios for risk-averse Kelly investments

Following a series of works on capital growth investment, we analyse log-optimal portfolios where the return evaluation includes `weights' of different outcomes. The results are twofold: (A) under certain conditions, the logarithmic growth rate leads to a supermartingale, and (B) the optimal (martingale) investment strategy is a proportional betting. We focus on properties of the optimal portfolios and discuss a number of simple examples extending the well-known Kelly betting scheme. An important restriction is that the investment does not exceed the current capital value and allows the trader to cover the worst possible losses. The paper deals with a class of discrete-time models. A continuous-time extension is a topic of an ongoing study

Irreducible Highest Weight Representations Of The Simple n-Lie Algebra

A. Dzhumadil'daev classified all irreducible finite dimensional representations of the simple n-Lie algebra. Using a slightly different approach, we obtain in this paper a complete classification of all irreducible, highest weight modules, including the infinite-dimensional ones. As a corollary we find all primitive ideals of the universal enveloping algebra of this simple n-Lie algebra.Comment: 24 pages, 24 figures, mistake in proposition 2.1 correcte

Using Regular Languages to Explore the Representational Capacity of Recurrent Neural Architectures

The presence of Long Distance Dependencies (LDDs) in sequential data poses significant challenges for computational models. Various recurrent neural architectures have been designed to mitigate this issue. In order to test these state-of-the-art architectures, there is growing need for rich benchmarking datasets. However, one of the drawbacks of existing datasets is the lack of experimental control with regards to the presence and/or degree of LDDs. This lack of control limits the analysis of model performance in relation to the specific challenge posed by LDDs. One way to address this is to use synthetic data having the properties of subregular languages. The degree of LDDs within the generated data can be controlled through the k parameter, length of the generated strings, and by choosing appropriate forbidden strings. In this paper, we explore the capacity of different RNN extensions to model LDDs, by evaluating these models on a sequence of SPk synthesized datasets, where each subsequent dataset exhibits a longer degree of LDD. Even though SPk are simple languages, the presence of LDDs does have significant impact on the performance of recurrent neural architectures, thus making them prime candidate in benchmarking tasks.Comment: International Conference of Artificial Neural Networks (ICANN) 201

Association of specific language impairment candidate genes CMIP and ATP2C2 with developmental dyslexia in Chinese population

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Association study of stuttering candidate genes GNPTAB, GNPTG and NAGPA with dyslexia in Chinese population

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A Transfer Matrix Method for Resonances in Randall-Sundrum Models

In this paper we discuss in detail a numerical method to study resonances in membranes generated by domain walls in Randall-Sundrum-like scenarios. It is based on similar works to understand the quantum mechanics of electrons subject to the potential barriers that exist in heterostructures in semiconductors. This method was used recently to study resonances of a three form field and lately generalized to arbitrary forms. We apply it to a lot of important models, namely those that contain the Gauge, Gravity and Spinor fields. In many cases we find a rich structure of resonances which depends on the parameters involved.Comment: 25 pages, 17 figure

A review of human error in marine engine maintenance

Maritime safety involves minimizing error in all aspects of the marine system. Human error hasreceived much importance, being responsible for about 80% of the maritime accident worldwide. Currently,more attention has been focused to reduce human error in marine engine maintenance. On-board marineengine maintenance activities are often complex, where seafarers conduct maintenance activities in variousmarine environmental (i.e. extreme weather, ship motions, noise, and vibration) and operational (i.e. workoverload and stress) conditions. These environmental and operational conditions, in combination with generichuman error tendencies, results in innumerable forms of error. There are numerous accidents that happeneddue to the human error during the maintenance activities of a marine engine. The most severe human errorresults in accidents due to is a loss of life. Moreover, there are other consequences too such as delaying theproductivity of marine operations which results in the financial loss. This study reviews methods that arecurrently available for identifying, reporting and managing human error in marine engine maintenance. As abasis for this discussion, authors provide an overview of approaches for investigating human error, and adescription of marine engine maintenance activities and environmental and operational characteristics