Stevens Institute of Technology

About: Stevens Institute of Technology is a education organization based out in Hoboken, New Jersey, United States. It is known for research contribution in the topics: Computer science & Cognitive radio. The organization has 5440 authors who have published 12684 publications receiving 296875 citations. The organization is also known as: Stevens & Stevens Tech.

Increasing storm tides in New York Harbor, 1844–2013

Abstract: Three of the nine highest recorded water levels in the New York Harbor region have occurred since 2010 (March 2010, August 2011, and October 2012), and eight of the largest twenty have occurred since 1990. To investigate whether this cluster of high waters is a random occurrence or indicative of intensified storm tides, we recover archival tide gauge data back to 1844 and evaluate the trajectory of the annual maximum storm tide. Approximately half of long-term variance is anticorrelated with decadal-scale variations in the North Atlantic Oscillation, while long-term trends explain the remainder. The 10 year storm tide has increased by 0.28 m. Combined with a 0.44 m increase in local sea level since 1856, the 10 year flood level has increased by approximately 0.72 ± 0.25 m, and magnified the annual probability of overtopping the typical Manhattan seawall from less than 1% to about 20–25%.

Comparison of HVOF and plasma-sprayed alumina/titania coatings—microstructure, mechanical properties and abrasion behavior

Abstract: We have evaluated the microstructure, mechanical properties and abrasion wear resistance of alumina/titania ceramic coatings deposited with nano- and micro-structured powders by high-velocity oxygen fuel (HVOF) and plasma spray (PS) processes. The deposition guns have a strong influence on the mechanical properties and abrasive wear resistance of the coatings, but the powders do not. The coatings deposited by HVOF are significantly harder and tougher, and their abrasion resistance is two–three-fold higher. Plastic microcutting plays the predominant role in abrasion wear of the coating deposited by HVOF. A combination of brittleness and porosity results in fracture that dominates the abrasion wear of plasma-sprayed coatings. The abrasion resistance measured follows an Evans–Marshall equation modified to account for the effects of porosity.

Optimal AUV path planning for extended missions in complex, fast-flowing estuarine environments

Abstract: This paper addresses the problems of automatically planning autonomous underwater vehicle (AUV) paths which best exploit complex current data, from computational estuarine model forecasts, while also avoiding obstacles. In particular we examine the possibilities for a novel type of AUV mission deployment in fast flowing tidal river regions which experience bi-directional current flow. These environments are interesting in that, by choosing an appropriate path in space and time, an AUV may both bypass adverse currents which are too fast to be overcome by the vehicle's motors and also exploit favorable currents to achieve far greater speeds than the motors could otherwise provide, while substantially saving energy. The AUV can "ride" currents both up and down the river, enabling extended monitoring of otherwise energy-exhausting, fast flow environments. The paper discusses suitable path parameterizations, cost functions and optimization techniques which enable optimal AUV paths to be efficiently generated. These paths take maximum advantage of the river currents in order to minimize energy expenditure, journey time and other cost parameters. The resulting path planner can automatically suggest useful alternative mission start and end times and locations to those specified by the user. Examples are presented for navigation in a simple simulation of the fast flowing Hudson River waters around Manhattan.

An adaptive portfolio trading system

Abstract: A reinforcement learning trading algorithm with expected drawdown risk is proposed.The expected maximum drawdown is shown to improve portfolio signal generation.The effectiveness of the method is validated using different transaction costs.An adaptive portfolio rebalancing system with automated retraining is recommended. Dynamic control theory has long been used in solving optimal asset allocation problems, and a number of trading decision systems based on reinforcement learning methods have been applied in asset allocation and portfolio rebalancing. In this paper, we extend the existing work in recurrent reinforcement learning (RRL) and build an optimal variable weight portfolio allocation under a coherent downside risk measure, the expected maximum drawdown, E(MDD). In particular, we propose a recurrent reinforcement learning method, with a coherent risk adjusted performance objective function, the Calmar ratio, to obtain both buy and sell signals and asset allocation weights. Using a portfolio consisting of the most frequently traded exchange-traded funds, we show that the expected maximum drawdown risk based objective function yields superior return performance compared to previously proposed RRL objective functions (i.e. the Sharpe ratio and the Sterling ratio), and that variable weight RRL long/short portfolios outperform equal weight RRL long/short portfolios under different transaction cost scenarios. We further propose an adaptive E(MDD) risk based RRL portfolio rebalancing decision system with a transaction cost and market condition stop-loss retraining mechanism, and we show that the proposed portfolio trading system responds to transaction cost effects better and outperforms hedge fund benchmarks consistently.

Engineers turn to e-learning

Abstract: Learning online is one of the strongest currents in higher education, as the working engineer in particular is discovering. This paper details how, in the coming years, technological advances, such as wireless networking, will undoubtedly help make e-learning more attractive. And as high-speed, broadband Internet connections become the norm, more real-time, interactive uses of the Web will appear in online-learning classes.