Showing papers by "National University of Defense Technology published in 2008"

Flame Characteristics in Supersonic Combustor with Hydrogen Injection Upstream of Cavity Flameholder

Abstract: Flame characteristics and a plausible flameholding mechanism in a supersonic combustor, with hydrogen injection upstream of a cavity flameholder, were investigated in the present study. Instantaneous OH radical distribution of the combustion flowfield was obtained using OH planar laser-induced fluorescence. According to the similarity of experimental observations with different cavities, a typical L/D = 7 cavity was chosen, and its supersonic combustion flowfield with hydrogen injection was calculated by large-eddy simulation. The results showed that the cavity shear layer plays a very important role in the flameholding process. An approximately steady flame existed in the cavity shear layer and hot combustion products were transported into the injection jet by the vortex interaction ofthejet-with-cavity shear layer. Flame then spread gradually following the counter-rotating vortex induced by the jet until the whole injection jet was ignited. Combustion products, which generated from the cavity shear layer and the jet, were convected into the cavity by the unsteady motion of the cavity shear layer and transported with the recirculation flow to the cavity front wall. These hot products and their intermittent combustion then heated up the cavity, and the fuel that entered into the cavity shear layer was preheated. Thus, the flameholding cycle was formed.

Underwater Localization in Sparse 3D Acoustic Sensor Networks

Abstract: We study the localization problem in sparse 3D underwater sensor networks. Considering the fact that depth information is typically available for underwater sensors, we transform the 3D underwater positioning problem into its two- dimensional counterpart via a projection technique and prove that a non-degenerative projection preserves network localizability. We further prove that given a network and a constant k, all of the geometric k-lateration localization methods are equivalent. Based on these results, we design a purely distributed localization framework termed USP. This framework can be applied with any ranging method proposed for 2D terrestrial sensor networks. Through theoretical analysis and extensive simulation, we show that USP preserves the localizability of the original 3D network via a simple projection and improves localization capabilities when bilateration is employed. USP has low storage and computation requirements, and predictable and balanced communication overhead.

Region-Based Classification of Polarimetric SAR Images Using Wishart MRF

Abstract: The scattering measurements of individual pixels in polarimetric SAR images are affected by speckle; hence, the performance of classification approaches, taking individual pixels as elements, would be damaged. By introducing the spatial relation between adjacent pixels, a novel classification method, taking regions as elements, is proposed using a Markov random field (MRF). In this method, an image is oversegmented into a large amount of rectangular regions first. Then, to use fully the statistical a priori knowledge of the data and the spatial relation of neighboring pixels, a Wishart MRF model, combining the Wishart distribution with the MRF, is proposed, and an iterative conditional mode algorithm is adopted to adjust oversegmentation results so that the shapes of all regions match the ground truth better. Finally, a Wishart-based maximum likelihood, based on regions, is used to obtain a classification map. Real polarimetric images are used in experiments. Compared with the other three frequently used methods, higher accuracy is observed, and classification maps are in better agreement with the initial ground maps, using the proposed method.

Adaptive Visual Servoing Using Point and Line Features With an Uncalibrated Eye-in-Hand Camera

Abstract: This paper presents a novel approach for image-based visual servoing of a robot manipulator with an eye-in-hand camera when the camera parameters are not calibrated and the 3-D coordinates of the features are not known. Both point and line features are considered. This paper extends the concept of depth-independent interaction (or image Jacobian) matrix, developed in earlier work for visual servoing using point features and fixed cameras, to the problem using eye-in-hand cameras and point and line features. By using the depth-independent interaction matrix, it is possible to linearly parameterize, by the unknown camera parameters and the unknown coordinates of the features, the closed-loop dynamics of the system. A new algorithm is developed to estimate unknown parameters online by combining the Slotine-Li method with the idea of structure from motion in computer vision. By minimizing the errors between the real and estimated projections of the feature on multiple images captured during motion of the robot, this new adaptive algorithm can guarantee the convergence of the estimated parameters to the real values up to a scale. On the basis of the nonlinear robot dynamics, we proved asymptotic convergence of the image errors by the Lyapunov theory. Experiments have been conducted to demonstrate the performance of the proposed controller.

Sweep coverage with mobile sensors

Abstract: Many efforts have been made for addressing coverage problems in sensor networks. They fall into two categories, full coverage and barrier coverage, featured as static coverage. In this work, we study a new coverage scenario, sweep coverage, which differs with the previous static coverage. In sweep coverage, we only need to monitor certain points of interest (POIs) periodically so the coverage at each POI is time-variant, and thus we are able to utilize a small number of mobile sensors to achieve sweep coverage among a much larger number of POIs. We investigate the definitions and model for sweep coverage. Given a set of POIs and their sweep period requirements, we prove that determining the minimum number of required sensors (min-sensor sweep-coverage problem) is NP-hard, and it cannot be approximated within a factor of 2. We propose a centralized algorithm with constant approximation ratio 2 + epsi for the simplified problem where all sweep periods are identical. We further characterize the non-locality of the problem and design a distributed sweep algorithm, DSWEEP, cooperating sensors to provide required sweep requirements with the best effort. We conduct extensive simulations to study the performance of the proposed algorithms. Our simulations show that DSWEEP outperforms the randomized scheme in both effectiveness and efficiency.

Globally Consistent Reconstruction of Ripped-Up Documents

Abstract: One of the most crucial steps for automatically reconstructing ripped-up documents is to find a globally consistent solution from the ambiguous candidate matches. However, little work has been done so far to solve this problem in a general computational framework without using application-specific features. In this paper, we propose a global approach for reconstructing ripped-up documents by first finding candidate matches from document fragments using curve matching and then disambiguating these candidates through a relaxation process to reconstruct the original document. The candidate disambiguation problem is formulated in a relaxation scheme in which the definition of compatibility between neighboring matches is proposed, and global consistency is defined as the global criterion. Initially, global match confidences are assigned to each of the candidate matches. After that, the overall local relationships among neighboring matches are evaluated by computing their global consistency. Then, these confidences are iteratively updated using the gradient projection method to maximize the criterion. This leads to a globally consistent solution and, thus, provides a sound document reconstruction. The overall performance of our approach in several practical experiments is illustrated. The results indicate that the reconstruction of ripped-up documents up to 50 pieces is possibly accomplished automatically.

Efficient available bandwidth estimation for network paths

Abstract: For estimating the available bandwidth along a network path,a novel active probing methodology COPP(chirp of packet paris)is presented combining self-induced congested principle with packet pair technique.After a sequence of packet pairs with gradually reduced intra-pair spacing was transmitted,COPP process would discover all turning bandwidth.Then every bandwidth was bound with a distinct weight according of the degree of interference of the underlying turning point and its neighbors to yield the final estimate by weighted adding up all turning bandwidth.Based on analysis of inter-packet spacing and one-way delay of single packet in a packet pair,the decision rules for determining whether a packet pair is turning point were shown.We also described the weighting process to obtain different weights for different kinds of turning point and discussed the effect of different thresholds on estimation.Simulation results in several cross traffic conditions indicate that COPP provides accurate estimation with relatively fewer overhead compared to other estimating tools.

Understanding and Summarizing Answers in Community-Based Question Answering Services

Abstract: Community-based question answering (cQA) services have accumulated millions of questions and their answers over time. In the process of accumulation, cQA services assume that questions always have unique best answers. However, with an in-depth analysis of questions and answers on cQA services, we find that the assumption cannot be true. According to the analysis, at least 78% of the cQA best answers are reusable when similar questions are asked again, but no more than 48% of them are indeed the unique best answers. We conduct the analysis by proposing taxonomies for cQA questions and answers. To better reuse the cQA content, we also propose applying automatic summarization techniques to summarize answers. Our results show that question-type oriented summarization techniques can improve cQA answer quality significantly.

Doubly heavy baryons in QCD sum rules

Abstract: The mass spectra of doubly heavy baryons are systematically calculated in the framework of QCD sum rules. With a tentative heavy-diquark--light-quark configuration, the interpolating currents representing the doubly heavy baryons are proposed. Contributions of the operators up to dimension six are included in operator product expansion. The numerical results are compatible with other theoretical predictions, which may support the $(QQ)\ensuremath{-}(q)$ structure of doubly heavy baryons.

Hybrid approach for solving systems of nonlinear equations using chaos optimization and quasi-Newton method

Abstract: Solving systems of nonlinear equations is one of the most difficult numerical computation problems. The convergences of the classical solvers such as Newton-type methods are highly sensitive to the initial guess of the solution. However, it is very difficult to select good initial solutions for most systems of nonlinear equations. By including the global search capabilities of chaos optimization and the high local convergence rate of quasi-Newton method, a hybrid approach for solving systems of nonlinear equations is proposed. Three systems of nonlinear equations including the ''Combustion of Propane'' problem are used to test our proposed approach. The results show that the hybrid approach has a high success rate and a quick convergence rate. Besides, the hybrid approach guarantees the location of solution with physical meaning, whereas the quasi-Newton method alone cannot achieve this.

Physics at BES-III

Abstract: This physics book provides detailed discussions on important topics in $\tau$-charm physics that will be explored during the next few years at \bes3 . Both theoretical and experimental issues are covered, including extensive reviews of recent theoretical developments and experimental techniques. Among the subjects covered are: innovations in Partial Wave Analysis (PWA), theoretical and experimental techniques for Dalitz-plot analyses, analysis tools to extract absolute branching fractions and measurements of decay constants, form factors, and CP-violation and \DzDzb-oscillation parameters. Programs of QCD studies and near-threshold tau-lepton physics measurements are also discussed.

Analysis of Lightning-Induced Voltages on Overhead Lines Using a 2-D FDTD Method and Agrawal Coupling Model

Abstract: In this paper, the lightning-generated electromagnetic fields over lossy ground produced by lightning strikes either to flat ground or to a tall tower are calculated using the 2-D finite-difference time-domain (FDTD) method. The resultant horizontal and vertical electric fields are used as forcing functions in the discretized Agrawal electromagnetic coupling equations for the calculation of induced voltages on overhead horizontal conductors without employing the Cooray-Rubinstein formula. Comparison of the results with those obtained using the 3-D FDTD method and with experimental data found in the literature is used to test the validity of the examined method. The approach employed here generally provides sufficient accuracy while allowing significant reduction in computation time and storage requirements as compared to the 3-D FDTD method. From the analysis carried out in this paper, induced voltages appear to be strongly dependent on ground conductivity, somewhat influenced by return-stroke speed, and essentially independent of return-stroke model [transmission-line (TL), modified transmission line with linear current decay with height (MTLL), or modified transmission line with exponential current decaywith height (MTLE)].

On the Construction of Boolean Functions With Optimal Algebraic Immunity

Abstract: In this correspondence, we introduce a method to construct Boolean functions in any number of variables, with optimal algebraic immunity. Remarkably, all functions of this type with an odd number of variables can be obtained in this way. We study some cryptographic properties, such as balancedness, algebraic degree of the constructed functions. Moreover, a lower bound of the number of Boolean functions with optimal algebraic immunity is given.

Barrier Coverage with Mobile Sensors

Abstract: Barrier coverage, which guarantees that every movement crossing a barrier of sensors will be detected, is known to be an appropriate model of coverage for moving detection and boundary guard. The related problems about barrier coverage with stationary sensors are extensively studied. When sensors are randomly deployed, we require much more sensors to achieve barrier coverage than deterministic deployment. In this paper we study barrier coverage with mobile sensors, in which the sensors can be relocated after deployment, and we are able to utilize much fewer mobile sensors than stationary sensors to achieve barrier coverage with random deployment. We study the energy-efficient relocation problem for barrier coverage, and propose a centralized barrier algorithm, which computes the relocated positions based on knowing the initial positions of all sensors. For practicability and scalability, we further design a distributed barrier algorithm based on our proposed virtual force model. We conduct extensive simulations to study the effectiveness of the proposed algorithms.

Trapped Rydberg ions: from spin chains to fast quantum gates

Abstract: We study the dynamics of Rydberg ions trapped in a linear Paul trap, and discuss the properties of ionic Rydberg states in the presence of the static and time-dependent electric fields constituting the trap. The interactions in a system of many ions are investigated and coupled equations of the internal electronic states and the external oscillator modes of a linear ion chain are derived. We show that strong dipole–dipole interactions among the ions can be achieved by microwave dressing fields. Using low-angular momentum states with large quantum defect, the internal dynamics can be mapped onto an effective spin model of a pair of dressed Rydberg states that describes the dynamics of Rydberg excitations in the ion crystal. We demonstrate that excitation transfer through the ion chain can be achieved on a nanosecond timescale and discuss the implementation of a fast two-qubit gate in the ion chain.

Heavy baryon spectroscopy in QCD

Abstract: We perform a systematic study of the masses of charmed and bottom baryons in the framework of the QCD sum rule approach. Contributions of the operators up to dimension six are included in operator product expansion. The resulting heavy baryon masses from the calculations are well consistent with the experimental values, and predictions to the spectroscopy of the unobserved bottom baryons are also presented.

A decomposition-based multi-objective Particle Swarm Optimization algorithm for continuous optimization problems

Abstract: Particle swarm optimization (PSO) is a heuristic optimization technique that uses previous personal best experience and global best experience to search global optimal solutions. This paper studies the application of PSO techniques to multi-objective optimization using decomposition methods. A new decomposition-based multi-objective PSO algorithm is proposed, called MOPSO/D. It integrates PSO into a multiobjective evolutionary algorithm based on decomposition (MOEA/D). The experimental results demonstrate that MOPSO/D can achieve better performance than a well-known MOEA, NSGA-II with differential evolution (DE), on most of the selected test instances. It shows that MOPSO/D will be a competitive candidate for multi-objective optimization.

Kinematic control of free rigid bodies using dual quaternions

Abstract: This paper proposes a new type of control laws for free rigid bodies. The start point is the dual quaternion and its characteristics. The logarithm of a dual quaternion is defined, based on which kinematic control laws can be developed. Global exponential convergence is achieved using logarithmic feedback via a generalized proportional control law, and an appropriate Lyapunov function is constructed to prove the stability. Both the regulation and tracking problems are tackled. Omnidirectional control is discussed as a case study. As the control laws can handle the interconnection between the rotation and translation of a rigid body, they are shown to be more applicable than the conventional method.

A Novel Compact Spiral Electromagnetic Band-Gap (EBG) Structure

Abstract: A novel compact electromagnetic band-gap (EBG) structure in a spiral shape is presented and investigated. This structure significantly enlarges the capacitance between neighboring elements. The simulations and experimental results have proved that the size of the spiral structure is only 30.9% of the conventional EBG structure. Two applications have been shown, including patch antenna with the spiral EBG structure and a double-element microstrip antenna array with low mutual coupling. The measured results show that a gain improvement over 3 dB and a significant reduction of cross polarization in H-plane are obtained. A 6 dB reduction of mutual coupling is achieved in a double-element EBG microstrip antenna array.

A High-Performance Feature-Matching Method for Image Registration by Combining Spatial and Similarity Information

Abstract: A crucial problem that involves feature-based image registration algorithms is how to reliably establish the correspondence between the features detected in the sensed image and those detected in the reference image. Generally, most existing methods only use spatial relations or feature similarity, or a simple combination of them, to solve this problem, and all have some limitations. In this paper, a new feature-matching strategy is developed. It is realized by introducing a function whose independent variable is the match matrix, which describes the correspondence of the features, to combine spatial relations and organically feature similarity, and its global maximum is assumed to be reached if the sensed image is completely aligned with the reference image. Thus, the feature correspondence can be estimated by finding the maximum of the function. Two approaches are devised to solve the optimization problem. One is based on the branch-and-bound strategy to yield a global optimal solution, and the other uses an iterative algorithm that combines graduated assignment and variable metric methods to search for a local optimal solution with low computational complexity. The proposed method can work without the limitations of feature type, similarity criterion, and transform model, and its performance is evaluated using a variety of real images. Compared with some existing methods, it is fast and robust, and has the highest accuracy.

Detecting the Sybil Attack Cooperatively in Wireless Sensor Networks

Abstract: Sybil attack is a network threat introduced by one or more malicious nodes to declare numerous illegal identifies to confuse or even collapse the network applications A new detection mechanism, called CRSD, is proposed for static wireless sensor networks, which takes use of the received signal strength (RSS) to infer the distance between two identities and further determines the positions relation of the interesting identities by use of the RSS information from multiple neighbor nodes, eg, via node cooperation A Sybil attack is detected when two or more different identities have almost the same position The analysis and simulation results show that, first, Sybil attack deteriorates the system performance significantly and second, CRSD can detect such attack in most cases, thus protecting the overall performance effectively

Dynamic Learning of SMLR for Feature Selection and Classification of Hyperspectral Data

Abstract: Feature selection is an important task in the analysis of hyperspectral data. Recently developed methods for learning sparse classifiers, which combine the automatic feature selection and classifier design, established themselves among the state of the art in the literature of machine learning. In this letter, the sparse multinomial logistic regression (SMLR) is introduced into the community of remote sensing and is utilized for the feature selection in the classification of hyperspectral data. To relieve the heavy degeneration of classification performance caused by the characteristics of the hyperspectral data and the oversparsity when the SMLR selects a small feature subset, we develop a dynamic learning framework to train the SMLR. Experimental results attest to the effectiveness of the proposed method.