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Hand Gesture Recognition Using Modified 1$ and Background Subtraction algorithms

MATHEMATICAL PROBLEMS IN ENGINEERING 2015年第1期2015卷 1-8页

作者： Khaled, Hazem Sayed, Samir G. Saad, El Sayed M. Ali, Hossam Helwan Univ Dept Elect & Commun Cairo 11795 Egypt Helwan Univ Dept Comp Engn Cairo 11795 Egypt UCL Dept Elect & Elect Engn London WC1E 6BT England NTRA Egyptian Comp Emergency Readiness Team EG CERT Cairo Egypt

computers and computerized machines have tremendously penetrated all aspects of our lives. This raises the importance of Human-computer Interface (HCI). The common HCI techniques still rely on simple devices such as keyboard, mice, and joysticks, which are not enough to convoy the latest technology. Hand gesture has become one of the most important attractive alternatives to existing traditional HCI techniques. This paper proposes a new hand gesture detection system for Human-computer Interaction using real-time video streaming. This is achieved by removing the background using average background algorithm and the 1$ algorithm for hand's template matching. Then every hand gesture is translated to commands that can be used to control robot movements. The simulation results show that the proposed algorithm can achieve high detection rate and small recognition time under different light changes, scales, rotation, and background.

关键词： GESTURE SUBTRACTION (Mathematics) computer algorithms HUMAN-computer interaction JOYSTICKS STREAMING video

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Sample-Based Extreme Learning Machine with Missing Data

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MATHEMATICAL PROBLEMS IN ENGINEERING 2015年第1期2015卷 1-11页

作者： Gao, Hang Liu, Xin-Wang Peng, Yu-Xing Jian, Song-Lei Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab Changsha 410073 Hunan Peoples R China

Extreme learning machine (ELM) has been extensively studied in machine learning community during the last few decades due to its high efficiency and the unification of classification, regression, and so forth. Though bearing such merits, existing ELM algorithms cannot efficiently handle the issue of missing data, which is relatively common in practical applications. The problem of missing data is commonly handled by imputation (i.e., replacing missing values with substituted values according to available information). However, imputation methods are not always effective. In this paper, we propose a sample-based learning framework to address this issue. Based on this framework, we develop two sample-based ELM algorithms for classification and regression, respectively. Comprehensive experiments have been conducted in synthetic data sets, UCI benchmark data sets, and a real world fingerprint image data set. As indicated, without introducing extra computational complexity, the proposed algorithms do more accurate and stable learning than other state-of-the-art ones, especially in the case of higher missing ratio.

关键词： MACHINE learning MISSING data (Statistics) REGRESSION analysis BIG data computer algorithms

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AN INCREMENTAL DECISION TREE FOR MINING MULTILABEL DATA

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APPLIED ARTIFICIAL INTELLIGENCE 2015年第10期29卷 992-1014页

作者： Li, Peipei Wu, Xindong Hu, Xuegang Wang, Hao Hefei Univ Technol Dept Comp Sci & Informat Engn Hefei 230009 Anhui Peoples R China Univ Vermont Dept Comp Sci Burlington VT USA

Mining with multilabel data is a popular topic in data mining. When performing classification on multilabel data, existing methods using traditional classifiers, such as support vector machines (SVMs), k-nearest neighbor (k-NN), and decision trees, have relatively poor accuracy and efficiency. Motivated by this, we present a new algorithm adaptation method, namely, a decision tree-based method for multilabel classification in domains with large-scale data sets called decision tree for multi-label classification (DTML). We build an incremental decision tree to reduce the learning time and divide the training data and adopt the k-NN classifier at leaves to improve the classification accuracy. Extensive studies show that our algorithm can efficiently learn from multilabel data while maintaining good performance on example-based evaluation metrics compared to nine state-of-the-art multilabel classification methods. Thus, we draw a conclusion that we provide an efficient and effective incremental algorithm adaptation method for multilabel classification especially in domains with large-scale multilabel data.

关键词： Decision trees Data mining computer algorithms Support vector machines K-nearest neighbor classification

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The BRUSH algorithm for two-electron integrals on GPU

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CHEMICAL PHYSICS LETTERS 2015年 622卷 92-98页

作者： Rak, Adam Cserey, Gyoergy Pazmany Peter Catholic Univ Fac Informat Technol & Bion H-1083 Budapest Hungary

This Letter presents a new algorithmic method developed to evaluate two-electron repulsion integrals based on contracted Gaussian basis functions in a parallel way. This new algorithm scheme provides distinct SIMD (single instruction multiple data) optimized paths which symbolically transforms integral parameters into target integral algorithms. Our measurements indicate that the method gives a significant improvement over the CPU-friendly PRISM algorithm. The benchmark tests (evaluation of more than 108 integrals using the STO-3G basis set) of our GPU (NVIDIA GTX 780) implementation showed up to 750-fold speedup compared to a single core of Athlon II X4 635 CPU. (c) 2015 Elsevier B.V. All rights reserved.

关键词： ELECTRONS GRAPHICS processing units (computers) GAUSSIAN processes RADIAL basis functions computer algorithms

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A multi-resolution approach for discovery and 3-D modeling of archaeological sites using satellite imagery and a UAV-borne camera

A multi-resolution approach for discovery and 3-D modeling o...

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American Control Conference

作者： Huanyu Ding Eric Cristofalo Joseph Wang David Castanon Eduardo Montijano Venkatesh Saligrama Mac Schwager Department of Electrical and Computer Engineering Boston University United States Department of Mechanical Engineering Boston University United States Centro Universitario de la Defensa (CUD) Zaragoza Spain Department of Aeronautics and Astronautics Stanford University United States

ISBN: (纸本)9781467386838

This paper proposes a method for discovering new archaeological sites from existing satellite imagery, then building a 3-D computer model of those sites using a controlled UAV with an onboard camera. We use an unmanned vehicle and other remote surveillance sensors, coupled with onboard pattern recognition algorithms, to perform a coarse search, and subsequently a fine search to identify structures of interest. We assume the availability of two sensors. The first sensor is a low resolution camera that sweeps an area of interest, such as an imaging satellite. We process the low-resolution image data to identify tentative locations of interest and to provide confidence estimates with this identification. This information is provided to a control algorithm for an unmanned air vehicle, which plans a trajectory to inspect closely promising objects subject to fuel constraints. These close inspections provide sequences of images that are combined to give 3-D reconstructions of the area of interest, leading to accurate classification of the structure. In this paper, we describe the design of the three principal algorithms in this system: machine learning processing of coarse resolution data, the near-optimal path planning subject to fuel constraints, and the high-resolution 3-D modeling from multiple 2-D views of a site. We illustrate the performance of our system on sample LANDSAT satellite data, and using a quadrotor with an on-board camera in a laboratory environment.

关键词： Unmanned aerial vehicles Three-dimensional displays Satellites Sensors Fuels Path planning Cameras three-dimensional displays Cameras Unmanned Aerial Vehicle Path planning Fuels Sensors satellites Satellite Imagery drone honey bees vehicle laboratory environment image sequences computer algorithms

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Real-time altitude optimization of airborne wind energy systems using Lyapunov-based switched extremum seeking control

Real-time altitude optimization of airborne wind energy syst...

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American Control Conference

作者： Alireza Bafandeh Chris Vermillion Mechanical Engineering and Engineering Science Department University of North Carolina at Charlotte USA Faculty of Mechanical Engineering and Engineering Science Department University of North Carolina at Charlotte United States

ISBN: (纸本)9781467386838

This paper applies a Lyapunov-based switched extremum seeking (LSES) control algorithm to the application of altitude optimization of airborne wind energy systems. We evaluate the stability and convergence of this control algorithm for the application at hand. Achievement of the optimal altitude to maximize energy production requires use of energy in order to search for the optimal altitude, which is continually and randomly varying. The simultaneous desires to optimize altitude and minimize control energy consumption are balanced in this paper through a variant of extremum seeking control, where the periodic perturbation signal is reduced when convergence upon an optimal altitude is detected. The signal is reinstated when the wind speed begins to deviate from its instantaneous optimal value. Because the wind shear profile (wind speed vs. altitude) is subject to continual variations, this application represents a challenging case study in LSES control. Using real wind shear data, the results presented in this paper show that the LSES controller is successful in significantly increasing the net energy production over fixed-altitude and standard extremum seeking strategies.

关键词： Wind speed Optimization Switches Wind energy generation Wind energy Wind energy generation Wind energy Switches extremum values wind power system wind speed computer algorithms Power production wind shear Optimal control Converge

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Back Analysis of the Permeability Coefficient of a High Core Rockfill Dam Based on a RBF Neural Network Optimized Using the PSO Algorithm

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MATHEMATICAL PROBLEMS IN ENGINEERING 2015年第1期2015卷 1-15页

作者： Chi, Shichun Ni, Shasha Liu, Zhenping Dalian Univ Technol Fac Infrastruct Engn Dalian 116024 Peoples R China Heilongjiang Inst Technol Coll Civil & Architecture Engn Harbin 150050 Peoples R China

It is important to determine the seepage field parameters of a high core rockfill dam using the seepage data obtained during operation. For the Nuozhadu high core rockfill dam, a back analysis model is proposed using the radial basis function neural network optimized using a particle swarm optimization algorithm (PSO-RBFNN) and the technology of finite element analysis for solving the saturated-unsaturated seepage field. The recorded osmotic pressure curves of osmometers, which are distributed in the maximum cross section, are applied to this back analysis. The permeability coefficients of the dam materials are retrieved using the measured seepage pressure values while the steady state seepage condition exists;that is, the water lever remains unchanged. Meanwhile, the parameters are tested using the unstable saturated-unsaturated seepage field while the water level rises. The results show that the permeability coefficients are reasonable and can be used to study the real behavior of a seepage field of a high core rockfill dam during its operation period.

关键词： PERMEABILITY COEFFICIENTS (Statistics) EARTH dams RADIAL basis functions NEURAL networks (computer science) PARTICLE swarm optimization computer algorithms

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Mobile Laboratory Observations of Methane Emissions in the Barnett Shale Region

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ENVIRONMENTAL SCIENCE & TECHNOLOGY 2015年第13期49卷 7889-7895页

作者： Yacovitch, Tara I. Herndon, Scott C. Petron, Gabrielle Kofler, Jonathan Lyon, David Zahniser, Mark S. Kolb, Charles E. Aerodyne Res Inc Billerica MA 01821 USA Univ Colorado Cooperat Inst Res Environm Sci Boulder CO 80309 USA NOAA Earth Syst Res Lab Boulder CO 80309 USA Environm Def Fund Austin TX 78701 USA

Results of mobile ground-based atmospheric measurements conducted during the Barnett Shale Coordinated Campaign in spring and fall of 2013 are presented. Methane and ethane are continuously measured downwind of facilities such as natural gas processing plants, compressor stations, and production well pads. Gaussian dispersion simulations of these methane plumes, using an iterative forward plume dispersion algorithm, are used to estimate both the source location and the emission magnitude. The distribution of emitters is peaked in the 0-5 kg/h range, with a significant tail. The ethane/methane molar enhancement ratio for this same distribution is investigated, showing a peak at similar to 1.5% and a broad distribution between similar to 4% and similar to 17%. The regional distributions of source emissions and ethane/methane enhancement ratios are examined: the largest methane emissions appear between Fort Worth and Dallas, while the highest ethane/methane enhancement ratios occur for plumes observed in the northwestern potion of the region. Individual facilities, focusing on large emitters, are further analyzed by constraining the source location.

关键词： Emissions (Air pollution) computer algorithms Methane -- Environmental aspects Plumes (Fluid dynamics) Ethanes

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Blink Number Forecasting Based on Improved Bayesian Fusion Algorithm for Fatigue Driving Detection

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MATHEMATICAL PROBLEMS IN ENGINEERING 2015年第1期2015卷 1-13页

作者： Sun, Wei Zhang, Xiaorui Wang, Jian He, Jun Peeta, Srinivas Nanjing Univ Informat Sci & Technol Sch Informat & Control Nanjing 210044 Jiangsu Peoples R China Jiangsu Key Lab Big Data Anal Technol Nanjing 210044 Jiangsu Peoples R China Nanjing Univ Informat Sci & Technol Sch Comp & Software Nanjing 210044 Jiangsu Peoples R China Purdue Univ NEXTRANS Ctr W Lafayette IN 47907 USA Nanjing Univ Informat Sci & Technol Sch Elect & Informat Engn Nanjing 210044 Jiangsu Peoples R China

An improved Bayesian fusion algorithm (BFA) is proposed for forecasting the blink number in a continuous video. It assumes that, at one prediction interval, the blink number is correlated with the blink numbers of only a few previous intervals. With this assumption, the weights of the component predictors in the improved BFA are calculated according to their prediction performance only from a few intervals rather than from all intervals. Therefore, compared with the conventional BFA, the improved BFA is more sensitive to the disturbed condition of the component predictors for adjusting their weights more rapidly. To determine the most relevant intervals, the grey relation entropy-based analysis (GREBA) method is proposed, which can be used analyze the relevancy between the historical data flows of blink number and the data flow at the current interval. Three single predictors, that is, the autoregressive integrated moving average (ARIMA), radial basis function neural network (RBFNN), and Kalman filter (KF), are designed and incorporated linearly into the BFA. Experimental results demonstrate that the improved BFA obviously outperforms the conventional BFA in both accuracy and stability;also fatigue driving can be accurately warned against in advance based on the blink number forecasted by the improved BFA.

关键词： NUMBER theory BAYESIAN analysis computer algorithms FATIGUE RADIAL basis functions NEURAL networks (computer science)

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Implementation of online maximum efficiency tracking control for a dual-motor drive system

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IET ELECTRIC POWER APPLICATIONS 2015年第7期9卷 449-458页

作者： Tseng, Shao-Kai Liu, Tian-Hua Hsu, Jing-Wei Ramelan, Luiz Rizki Firmansyah, Eka Natl Taiwan Univ Sci & Technol Dept Elect Engn Taipei Taiwan Gadjah Mada Univ Dept Elect Engn & Informat Technol Yogyakarta Indonesia

This paper proposes an online maximum efficiency tracking control for a dual-motor drive system. The efficiency of each drive system is obtained by online measuring the input power and online estimating the output power. Two algorithms, including the steepest ascent algorithm and fuzzy logic algorithm are proposed to achieve maximum efficiency tracking. The proposed methods do not require any computations related to the copper and core losses of the motor and the switching and conduction losses of the inverter. In addition, the proposed drive system can have better efficiency and better flexibility and requires smaller power ratings of each motor and inverter, which are easily manufactured in industry. The control algorithms are executed by using two digital signal processors, type TMS 320-F2808, made by Texas Instruments Incorporated Company. A controller area network (CAN) bus is used as a communication tool. Several experimental results show the correctness and feasibility of the proposed method. The proposed method can be easily applied in industry because of its simplicity.

关键词： maximum efficiency Conduction losses accuracy class Power rating Fuzzy logic tracking control Motor Core loss drive train Inventors Communication tools computer algorithms

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