Showing papers by "National University of Defense Technology published in 2011"
TL;DR: A survey on the development of D2ITS is provided, discussing the functionality of its key components and some deployment issues associated with D2 ITS Future research directions for the developed system are presented.
Abstract: For the last two decades, intelligent transportation systems (ITS) have emerged as an efficient way of improving the performance of transportation systems, enhancing travel security, and providing more choices to travelers. A significant change in ITS in recent years is that much more data are collected from a variety of sources and can be processed into various forms for different stakeholders. The availability of a large amount of data can potentially lead to a revolution in ITS development, changing an ITS from a conventional technology-driven system into a more powerful multifunctional data-driven intelligent transportation system (D2ITS) : a system that is vision, multisource, and learning algorithm driven to optimize its performance. Furthermore, D2ITS is trending to become a privacy-aware people-centric more intelligent system. In this paper, we provide a survey on the development of D2ITS, discussing the functionality of its key components and some deployment issues associated with D2ITS Future research directions for the development of D2ITS is also presented.
1,336 citations
TL;DR: This paper found that emotional tweet percentage significantly negatively correlated with Dow Jones, NASDAQ and S&P 500, but displayed a significant positive correlation to VIX, and that just checking on twitter for emotional outbursts of any kind gives a predictor of how the stock market will be doing the next day.
573 citations
TL;DR: A comprehensive review of Uncertainty-Based Multidisciplinary Design Optimization (UMDO) theory and the state of the art in UMDO methods for aerospace vehicles is presented.
426 citations
TL;DR: The manifold regularization and the margin maximization to NMF are introduced and the manifold regularized discriminative NMF (MD-NMF) is obtained to overcome the aforementioned problems.
Abstract: Nonnegative matrix factorization (NMF) has become a popular data-representation method and has been widely used in image processing and pattern-recognition problems. This is because the learned bases can be interpreted as a natural parts-based representation of data and this interpretation is consistent with the psychological intuition of combining parts to form a whole. For practical classification tasks, however, NMF ignores both the local geometry of data and the discriminative information of different classes. In addition, existing research results show that the learned basis is unnecessarily parts-based because there is neither explicit nor implicit constraint to ensure the representation parts-based. In this paper, we introduce the manifold regularization and the margin maximization to NMF and obtain the manifold regularized discriminative NMF (MD-NMF) to overcome the aforementioned problems. The multiplicative update rule (MUR) can be applied to optimizing MD-NMF, but it converges slowly. In this paper, we propose a fast gradient descent (FGD) to optimize MD-NMF. FGD contains a Newton method that searches the optimal step length, and thus, FGD converges much faster than MUR. In addition, FGD includes MUR as a special case and can be applied to optimizing NMF and its variants. For a problem with 165 samples in R1600 , FGD converges in 28 s, while MUR requires 282 s. We also apply FGD in a variant of MD-NMF and experimental results confirm its efficiency. Experimental results on several face image datasets suggest the effectiveness of MD-NMF.
312 citations
TL;DR: A mathematical model is presented and a Lagrangian relaxation based approach is developed to solve the production planning problem for a multi-product closed loop system, in which the manufacturer has two channels for supplying products.
263 citations
TL;DR: A scheme that recovers the (dynamic) Bayesian dependency graph (connections in a network) using observed network activity is described that furnishes a network description of distributed activity in the brain that is optimal in the sense of having the greatest conditional probability, relative to other networks.
260 citations
10 Apr 2011
TL;DR: IMDGuard is introduced, a comprehensive security scheme for heart-related IMDs to fulfill the requirement to remain operable in an emergency when appropriate security credentials may be unavailable and incorporates two techniques tailored to provide desirable protections for IMDs.
Abstract: Recent studies have revealed security vulnerabilities in implantable medical devices (IMDs). Security design for IMDs is complicated by the requirement that IMDs remain operable in an emergency when appropriate security credentials may be unavailable. In this paper, we introduce IMDGuard, a comprehensive security scheme for heart-related IMDs to fulfill this requirement. IMDGuard incorporates two techniques tailored to provide desirable protections for IMDs. One is an ECG based key establishment without prior shared secrets, and the other is an access control mechanism resilient to adversary spoofing attacks. The security and performance of IMDGuard are evaluated on our prototype implementation.
240 citations
TL;DR: A synthetic aperture radar (SAR) automatic target recognition approach based on a global scattering center model that is much easier to implement and less sensitive to nonideal factors such as noise and pose estimation error than point-to-point matching is proposed.
Abstract: This paper proposes a synthetic aperture radar (SAR) automatic target recognition approach based on a global scattering center model. The scattering center model is established offline using range profiles at multiple viewing angles, so the original data amount is much less than that required for establishing SAR image templates. Scattering center features at different target poses can be conveniently predicted by this model. Moreover, the model can be modified to predict features for various target configurations. For the SAR image to be classified, regional features in different levels are extracted by thresholding and morphological operations. The regional features will be matched to the predicted scattering center features of different targets to arrive at a decision. This region-to-point matching is much easier to implement and is less sensitive to nonideal factors such as noise and pose estimation error than point-to-point matching. A matching scheme going through from coarse to fine regional features in the inner cycle and going through different pose hypotheses in the outer cycle is designed to improve the efficiency and robustness of the classifier. Experiments using both data predicted by a high-frequency electromagnetic (EM) code and data measured in the MSTAR program verify the validity of the method.
217 citations
07 Dec 2011
TL;DR: This paper proposes an efficient parallel density-based clustering algorithm and implements it by a 4-stages MapReduce paradigm and adopts a quick partitioning strategy for large scale non-indexed data.
Abstract: Data clustering is an important data mining technology that plays a crucial role in numerous scientific applications. However, it is challenging due to the size of datasets has been growing rapidly to extra-large scale in the real world. Meanwhile, MapReduce is a desirable parallel programming platform that is widely applied in kinds of data process fields. In this paper, we propose an efficient parallel density-based clustering algorithm and implement it by a 4-stages MapReduce paradigm. Furthermore, we adopt a quick partitioning strategy for large scale non-indexed data. We study the metric of merge among bordering partitions and make optimizations on it. At last, we evaluate our work on real large scale datasets using Hadoop platform. Results reveal that the speedup and scale up of our work are very efficient.
213 citations
01 Nov 2011
TL;DR: The proposed natural connectivity provides sensitive discrimination of structural robustness that agrees with intuition within a scenario of edge elimination and is calculated both analytically and numerically the natural connectivity of three typical networks.
Abstract: We introduce the concept of natural connectivity as a measure of structural robustness in complex networks. The natural connectivity characterizes the redundancy of alternative routes in a network by quantifying the weighted number of closed walks of all lengths. This definition leads to a simple mathematical formulation that links the natural connectivity to the spectrum of a network. The natural connectivity can be regarded as an average eigenvalue that changes strictly monotonically with the addition or deletion of edges. We calculate both analytically and numerically the natural connectivity of three typical networks: regular ring lattices, random graphs, and random scale-free networks. We also compare the proposed natural connectivity to other structural robustness measures within a scenario of edge elimination and demonstrate that the natural connectivity provides sensitive discrimination of structural robustness that agrees with our intuition.
204 citations
TL;DR: This paper proposes to use a bivariate Birnbaum–Saunders distribution and its marginal distributions to approximate the reliability function of a product that has two dependent performance characteristics and that their degradation can be modeled by gamma processes.
TL;DR: In this paper, the authors analyzed the difficulties in producing a long-pulse HPM for the O-type Cerenkov HPM source, and suggested that explosive emissions on surfaces of designed eletrodynamic structures restrained pulse duration and operation stability.
Abstract: Recent experimental results of three kinds of long-pulse high-power microwave (HPM) sources operating in S-, C-, and X-bands are reported. The difficulties in producing a long-pulse HPM for the O-type Cerenkov HPM source were analyzed theoretically. In S- and C-bands, single-mode relativistic backward-wave oscillators were designed to achieve long-pulse HPM outputs; in X-band, because of its shorter wavelength, an O-type Cerenkov HPM source with overmoded slow-wave systems was designed to increase power capacity. In experiments, driven by a repetitive long-pulse accelerator, both S- and C-band sources generated HPMs with power of about 2 GW and pulse duration of about 100 ns in single-shot mode, and the S-band source operated stably with output power of 1.2 GW in 20-Hz repetition mode. The X-band source generated 2 GW microwaves power with pulse duration of 80 ns in the single-shot mode and 1.2 GW microwave power with pulse duration of about 100 ns in the 20-Hz repetition mode. The experiments show good performances of the O-type Cerenkov HPM source in generating repetitive long-pulse HPMs, especially in S- and C-bands. It was suggested that explosive emissions on surfaces of designed eletrodynamic structures restrained pulse duration and operation stability.
TL;DR: The ability to model endogenous or random fluctuations on hidden neuronal (and physiological) states provides a new and possibly more plausible perspective on how regionally specific signals in fMRI are generated.
TL;DR: It is shown that the INS attitude alignment can be equivalently transformed into a “continuous” attitude determination problem using infinite vector observations.
04 Jun 2011
TL;DR: This work designs a novel low-cost congestion propagation network that leverages both local and non-local network information for more accurate congestion estimates and offers effective adaptivity for congestion beyond neighboring nodes, and proposes Destination-Based Adaptive Routing (DBAR).
Abstract: With the emergence of many-core architectures, it is quite likely that multiple applications will run concurrently on a system. Existing locally and globally adaptive routing algorithms largely overlook issues associated with workload consolidation. The shortsightedness of locally adaptive routing algorithms limits performance due to poor network congestion avoidance. Globally adaptive routing algorithms attack this issue by introducing a congestion propagation network to obtain network status information beyond neighboring nodes. However, they may suffer from intra- and inter-application interference during output port selection for consolidated workloads, coupling the behavior of otherwise independent applications and negatively affecting performance. To address these two issues, we propose Destination-Based Adaptive Routing (DBAR). We design a novel low-cost congestion propagation network that leverages both local and non-local network information for more accurate congestion estimates. Thus, DBAR offers effective adaptivity for congestion beyond neighboring nodes. More importantly, by integrating the destination into the selection function, DBAR mitigates intra- and inter-application interference and offers dynamic isolation among regions. Experimental results show that DBAR can offer better performance than the best baseline algorithm for all measured configurations; it is well suited for workload consolidation. The wiring overhead of DBAR is low and DBAR provides improvement in the energy-delay product for medium and high injection rates.
TL;DR: A fast gradient descent (FGD) is proposed to overcome the problem of slow multiplicative update rule in NPAF and uses the Newton method to search the optimal step size, and thus converges faster than MUR.
Abstract: In this paper, we present a non-negative patch alignment framework (NPAF) to unify popular non-negative matrix factorization (NMF) related dimension reduction algorithms. It offers a new viewpoint to better understand the common property of different NMF algorithms. Although multiplicative update rule (MUR) can solve NPAF and is easy to implement, it converges slowly. Thus, we propose a fast gradient descent (FGD) to overcome the aforementioned problem. FGD uses the Newton method to search the optimal step size, and thus converges faster than MUR. Experiments on synthetic and real-world datasets confirm the efficiency of FGD compared with MUR for optimizing NPAF. Based on NPAF, we develop non-negative discriminative locality alignment (NDLA). Experiments on face image and handwritten datasets suggest the effectiveness of NDLA in classification tasks and its robustness to image occlusions, compared with representative NMF-related dimension reduction algorithms.
TL;DR: This paper proposes a general adaptive incremental learning framework named ADAIN that is capable of learning from continuous raw data, accumulating experience over time, and using such knowledge to improve future learning and prediction performance.
Abstract: Recent years have witnessed an incredibly increasing interest in the topic of incremental learning Unlike conventional machine learning situations, data flow targeted by incremental learning becomes available continuously over time Accordingly, it is desirable to be able to abandon the traditional assumption of the availability of representative training data during the training period to develop decision boundaries Under scenarios of continuous data flow, the challenge is how to transform the vast amount of stream raw data into information and knowledge representation, and accumulate experience over time to support future decision-making process In this paper, we propose a general adaptive incremental learning framework named ADAIN that is capable of learning from continuous raw data, accumulating experience over time, and using such knowledge to improve future learning and prediction performance Detailed system level architecture and design strategies are presented in this paper Simulation results over several real-world data sets are used to validate the effectiveness of this method
16 Jul 2011
TL;DR: This paper introduces a novel unsupervised feature selection approach via Joint Embedding Learning and Sparse Regression (JELSR), which uses the weight via locally linear approximation to construct graph and unify embedding learning and sparse regression to perform feature selection.
Abstract: The problem of feature selection has aroused considerable research interests in the past few years. Traditional learning based feature selection methods separate embedding learning and feature ranking. In this paper, we introduce a novel unsupervised feature selection approach via Joint Embedding Learning and Sparse Regression (JELSR). Instead of simply employing the graph laplacian for embedding learning and then regression, we use the weight via locally linear approximation to construct graph and unify embedding learning and sparse regression to perform feature selection. By adding the l2,1-norm regularization, we can learn a sparse matrix for feature ranking. We also provide an effective method to solve the proposed problem. Compared with traditional unsupervised feature selection methods, our approach could integrate the merits of embedding learning and sparse regression simultaneously. Plenty of experimental results are provided to show the validity.
TL;DR: The history of the development of traffic control and management systems within the evolving computing paradigm is reviewed and the state of traffic Control and Management systems based on mobile multiagent technology is shown.
Abstract: Agent-based traffic management systems can use the autonomy, mobility, and adaptability of mobile agents to deal with dynamic traffic environments. Cloud computing can help such systems cope with the large amounts of storage and computing resources required to use traffic strategy agents and mass transport data effectively. This article reviews the history of the development of traffic control and management systems within the evolving computing paradigm and shows the state of traffic control and management systems based on mobile multiagent technology.
TL;DR: An overview of TianHe- 1A (TH-1A) supercomputer, which is built by National University of Defense Technology of China (NUDT), is presented, which was ranked the No. 1 on the TOP500 List released in November, 2010.
Abstract: This paper presents an overview of TianHe-1A (TH-1A) supercomputer, which is built by National University of Defense Technology of China (NUDT). TH-1A adopts a hybrid architecture by integrating CPUs and GPUs, and its interconnect network is a proprietary high-speed communication network. The theoretical peak performance of TH-1A is 4700 TFlops, and its LINPACK test result is 2566 TFlops. It was ranked the No. 1 on the TOP500 List released in November, 2010. TH-1A is now deployed in National Supercomputer Center in Tianjin and provides high performance computing services. TH-1A has played an important role in many applications, such as oil exploration, weather forecast, bio-medical research.
TL;DR: It is shown that symmetry hierarchy naturally implies a hierarchical segmentation that is more meaningful than those produced by local geometric considerations, and an application of symmetry hierarchies for structural shape editing is developed.
Abstract: We introduce symmetry hierarchy of man-made objects, a high-level structural representation of a 3D model providing a symmetry-induced, hierarchical organization of the model’s constituent parts. Given an input mesh, we segment it into primitive parts and build an initial graph which encodes inter-part symmetries and connectivity relations, as well as self-symmetries in individual parts. The symmetry hierarchy is constructed from the initial graph via recursive graph contraction which either groups parts by symmetry or assembles connected sets of parts. The order of graph contraction is dictated by a set of precedence rules designed primarily to respect the law of symmetry in perceptual grouping and the principle of compactness of representation. We show that symmetry hierarchy naturally implies a hierarchical segmentation that is more meaningful than those produced by local geometric considerations. We also develop an application of symmetry hierarchies for structural shape editing.
TL;DR: In this article, the band gap mechanisms of a uniform string with periodically attached spring-mass resonators are investigated. But the authors focus on the band-gap mechanism of a simple locally resonant continuous elastic system whose band gap mechanism is basic to more general and complicated problems.
TL;DR: This paper proposes a new method, called spectral multi-manifold clustering (SMMC), which is able to handle intersections, and demonstrates the promising performance of this method on synthetic as well as real datasets.
Abstract: Spectral clustering (SC) is a large family of grouping methods that partition data using eigenvectors of an affinity matrix derived from the data. Though SC methods have been successfully applied to a large number of challenging clustering scenarios, it is noteworthy that they will fail when there are significant intersections among different clusters. In this paper, based on the analysis that SC methods are able to work well when the affinity values of the points belonging to different clusters are relatively low, we propose a new method, called spectral multi-manifold clustering (SMMC), which is able to handle intersections. In our model, the data are assumed to lie on or close to multiple smooth low-dimensional manifolds, where some data manifolds are separated but some are intersecting. Then, local geometric information of the sampled data is incorporated to construct a suitable affinity matrix. Finally, spectral method is applied to this affinity matrix to group the data. Extensive experiments on synthetic as well as real datasets demonstrate the promising performance of SMMC.
TL;DR: Coherent beam combination of a 1.08 kW fiber amplifier array has been demonstrated for the first time and the fringe contrast of the far-field intensity pattern is improved to more than 85%, and the residual phase error is less than λ/15.
Abstract: Coherent beam combination of a 1.08 kW fiber amplifier array has been demonstrated for the first time, to our knowledge. In the experiment, nine fiber amplifiers are tiled into a 3×3 array, and the output power of each amplifier is approximately 120 W. A single frequency dithering algorithm is used to compensate the phase noises between the elements, which runs on a signal processor based on field programmable gate array for phase control on the fiber amplifiers. When the phase control system goes into closed loop, the fringe contrast of the far-field intensity pattern is improved to more than 85%, and the residual phase error is less than λ/15.
TL;DR: The article provides a preliminary account of the operational process of command and control based on the cyber-physical-social system (CPSS) and a self-synchronization mechanism and connects the physical network, cyberspace, mental space, and social network.
Abstract: The article provides a preliminary account of the operational process of command and control based on the cyber-physical-social system (CPSS) and a self-synchronization mechanism. The proposed CPSS for command and control incorporates the essential characteristics of operational mechanism and connects the physical network, cyberspace, mental space, and social network.
TL;DR: The finite element method is introduced to investigate the dynamic modes and the corresponding sound absorption of localized resonance and the absorption properties of viscoelastic materials containing locally resonant scatterers with ellipsoidal shape are discussed.
Abstract: Recently, by introducing locally resonant scatterers with spherical shape proposed in phononic crystals into design of underwater sound absorption materials, the low-frequency underwater sound absorption phenomenon induced by the localized resonances is observed. To reveal this absorption mechanism, the effect of the locally resonant mode on underwater sound absorption should be studied. In this paper, the finite element method, which is testified efficiently by comparing the calculation results with those of the layer multiple scattering method, is introduced to investigate the dynamic modes and the corresponding sound absorption of localized resonance. The relationship between the resonance modes described with the displacement contours of one unit cell and the corresponding absorption spectra is discussed in detail, which shows that the localized resonance leads to the absorption peak, and the mode conversion from longitudinal to transverse waves at the second absorption peak is more efficient than that at the first one. Finally, to show the modeling capability of FEM and investigate shape effects of locally resonant scatterers on underwater sound absorption, the absorption properties of viscoelastic materials containing locally resonant scatterers with ellipsoidal shape are discussed.
TL;DR: This work studies a new coverage scenario, sweep coverage, which differs with the previous static coverage, and proposes a centralized algorithm with constant approximation ratio 3 for the min-sensor sweep-coverage problem, and designs a distributed sweep algorithm, DSWEEP, cooperating sensors to provide efficiency with the best effort.
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 3 for the min-sensor sweep-coverage problem. We further characterize the nonlocality of the problem and design a distributed sweep algorithm, DSWEEP, cooperating sensors to provide efficiency 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.
TL;DR: The integral inequality lemma is applied to extend research to more general Nakagami-m fading channel and can be applied to evaluate the average packet error rate of a conventional packet transmission system over a quasi static fading channel.
Abstract: We propose a new analytical approach to evaluate the average packet error rate (PER) of a conventional packet transmission system over a quasi static fading channel, by presenting an integral inequality lemma. The basic idea of the approach is that, given the PER for the AWGN channel as a function of signal-to-noise ratio (SNR), the average PER over Rayleigh fading channel can be generally upper bounded by a quite simple inequality, i.e.,1 - exp(-wo/γ), for both coded and uncoded schemes, where wo, defined by an integral expression, corresponds exactly to the inversion of coding gain; and this bound is tight in the high SNR region or for long packet systems. We further apply the integral inequality to extend our research to more general Nakagami-m fading channel.
TL;DR: The experiments show that the RD reputation improves the reliability of an application with more accurate reputations, while the LAGA provides better solutions than existing list heuristics and evolves to better solutions more quickly than a traditional GA.
TL;DR: In this paper, the effects of the divergent angle and the back pressure on the shock wave transition and the location of the leading edge of the turbojet in a three-dimensional scramjet isolator were estimated and discussed.