Showing papers by "Northeastern University (China) published in 2010"
TL;DR: In this article, N-doped multi-layered graphene sheets were synthesized in large scale by the method of direct current arc-discharge between pure graphite rods.
393 citations
TL;DR: New delay-dependent stability criteria for RNNs with time-varying delay are derived by applying this weighting-delay method, which are less conservative than previous results.
Abstract: In this paper, a weighting-delay-based method is developed for the study of the stability problem of a class of recurrent neural networks (RNNs) with time-varying delay. Different from previous results, the delay interval [0, d(t)] is divided into some variable subintervals by employing weighting delays. Thus, new delay-dependent stability criteria for RNNs with time-varying delay are derived by applying this weighting-delay method, which are less conservative than previous results. The proposed stability criteria depend on the positions of weighting delays in the interval [0, d(t)], which can be denoted by the weighting-delay parameters. Different weighting-delay parameters lead to different stability margins for a given system. Thus, a solution based on optimization methods is further given to calculate the optimal weighting-delay parameters. Several examples are provided to verify the effectiveness of the proposed criteria.
374 citations
TL;DR: This paper presents an artificial bee colony clustering algorithm to optimally partition N objects into K clusters, using the Deb's rules to direct the search direction of each candidate.
Abstract: Clustering is a popular data analysis and data mining technique. In this paper, an artificial bee colony clustering algorithm is presented to optimally partition N objects into K clusters. The Deb's rules are used to direct the search direction of each candidate. This algorithm has been tested on several well-known real datasets and compared with other popular heuristics algorithm in clustering, such as GA, SA, TS, ACO and the recently proposed K-NM-PSO algorithm. The computational simulations reveal very encouraging results in terms of the quality of solution and the processing time required.
345 citations
01 Jun 2010
TL;DR: A novel impulsive control scheme (so-called dual-stage impulsiveControl) is proposed, based on the theory of impulsive functional differential equations, to guarantee that the synchronization error dynamics can converge to a predetermined level.
Abstract: This paper is concerned with the robust exponential synchronization problem of a class of chaotic delayed neural networks with different parametric uncertainties. A novel impulsive control scheme (so-called dual-stage impulsive control) is proposed. Based on the theory of impulsive functional differential equations, a global exponential synchronization error bound together with some new sufficient conditions expressed in the form of linear matrix inequalities (LMIs) is derived in order to guarantee that the synchronization error dynamics can converge to a predetermined level. Furthermore, to estimate the stable region, a novel optimization control algorithm is established, which can deal with the minimum problem with two nonlinear terms coexisting in LMIs effectively. The idea and approach developed in this paper can provide a more practical framework for the synchronization of multiperturbation delayed chaotic systems. Simulation results finally demonstrate the effectiveness of the proposed method.
343 citations
TL;DR: A new method for designing indirect adaptive reliable controller via state feedback is presented for actuator fault compensations, and a notion of adaptive H ∞ performance index is proposed to describe the disturbance attenuation performances of closed-loop systems.
Abstract: This technical note studies the problem of designing reliable H ∞ controllers with adaptive mechanism for linear systems. A new method for designing indirect adaptive reliable controller via state feedback is presented for actuator fault compensations. Based on the on-line estimation of eventual faults, the proposed reliable controller parameters are updated automatically to compensate the fault effects on systems. A notion of adaptive H ∞ performance index is proposed to describe the disturbance attenuation performances of closed-loop systems. The design conditions are given in terms of solutions to a set of linear matrix inequalities (LMIs). The resultant designs can guarantee the asymptotic stability and adaptive H ∞ performances of closed-loop systems even in the cases of actuator failures. The effectiveness of the proposed design method is illustrated via a numerical example.
294 citations
16 Sep 2010
TL;DR: This paper presented an empirical study on the effects of budgetary goal characteristics on managerial attitudes and performance in two dimensions of goal clarity and goal difficulty, and found that budget goal difficulty and budget goal clarity have significant effects on job-related and budget-related attitudes.
Abstract: This study presents an empirical study on the effects of budgetary goal characteristics on managerial attitudes and performance in two dimensions of goal clarity and goal difficulty. The results show that budget goal difficulty and budget goal clarity have significant effects on job-related and budget-related attitudes. Furthermore, budget goal difficulty is found to have a positive effect on managerial performance. High level of budget goal difficulty and budget goal clarity will lead to a high degree of budgetary incentives.
282 citations
TL;DR: Application results indicate that the proposed decentralized monitoring scheme effectively captures the complex relations in the process and improves the diagnosis ability tremendously.
Abstract: In this paper, a decentralized fault diagnosis approach of complex processes is proposed based on multiblock kernel partial least squares (MBKPLS). To solve the problem posed by nonlinear characteristics, kernel partial least squares (KPLS) approaches have been proposed. In this paper, MBKPLS algorithm is first proposed and applied to monitor large-scale processes. The advantages of MBKPLS are: 1) MBKPLS can capture more useful information between and within blocks compared to partial least squares (PLS); 2) MBKPLS gives nonlinear interpretation compared to MBPLS; 3) Fault diagnosis becomes possible if number of sub-blocks is equal to the number of the variables compared to KPLS. The proposed methods are applied to process monitoring of a continuous annealing process. Application results indicate that the proposed decentralized monitoring scheme effectively captures the complex relations in the process and improves the diagnosis ability tremendously.
260 citations
225 citations
05 Jul 2010
TL;DR: A novel dynamic provisioning technique for a cluster-based virtualized multi-tier application that employ a flexible hybrid queueing model to determine the number of virtual machines at each tier in a virtualized application is presented.
Abstract: Dynamic provisioning is a useful technique for handling the virtualized multi-tier applications in cloud environment. Understanding the performance of virtualized multi-tier applications is crucial for efficient cloud infrastructure management. In this paper, we present a novel dynamic provisioning technique for a cluster-based virtualized multi-tier application that employ a flexible hybrid queueing model to determine the number of virtual machines at each tier in a virtualized application. We present a cloud data center based on virtual machine to optimize resources provisioning. Using simulation experiments of three-tier application, we adopt an optimization model to minimize the total number of virtual machines while satisfying the customer average response time constraint and the request arrival rate constraint. Our experiments show that cloud data center resources can be allocated accurately with these techniques, and the extra cost can be effectively reduced.
191 citations
TL;DR: In this article, a pseudocapacitive study of a composite film (PANI-ND-MnO 2 ) of polyaniline and manganese oxide nanoparticles was presented.
183 citations
TL;DR: In this article, a numerical model capable of studying the dynamic failure process of rock under coupled static geo-stress and dynamic disturbance is proposed, and it is implemented into the Rock Failure Process Analysis (RFPA), a general finite element package to analyze the damage and failure of engineering materials such as rock and concrete.
TL;DR: This work proposes dynamic Bloom filters to represent dynamic sets, as well as static sets and design necessary item insertion, membership query, item deletion, and filter union algorithms.
Abstract: A Bloom filter is an effective, space-efficient data structure for concisely representing a set, and supporting approximate membership queries. Traditionally, the Bloom filter and its variants just focus on how to represent a static set and decrease the false positive probability to a sufficiently low level. By investigating mainstream applications based on the Bloom filter, we reveal that dynamic data sets are more common and important than static sets. However, existing variants of the Bloom filter cannot support dynamic data sets well. To address this issue, we propose dynamic Bloom filters to represent dynamic sets, as well as static sets and design necessary item insertion, membership query, item deletion, and filter union algorithms. The dynamic Bloom filter can control the false positive probability at a low level by expanding its capacity as the set cardinality increases. Through comprehensive mathematical analysis, we show that the dynamic Bloom filter uses less expected memory than the Bloom filter when representing dynamic sets with an upper bound on set cardinality, and also that the dynamic Bloom filter is more stable than the Bloom filter due to infrequent reconstruction when addressing dynamic sets without an upper bound on set cardinality. Moreover, the analysis results hold in stand-alone applications, as well as distributed applications.
TL;DR: Inspired by the swarm intelligence of particle swarm, a novel global harmony search algorithm (NGHS) is proposed to solve reliability problems and has demonstrated stronger capacity of space exploration than most other approaches on solving reliability problems.
TL;DR: The carboxyl groups on the nanocomposite surface were proved to be chemically active and readily available for further bioconjugation with biomolecules such as bovine serum albumin and antibodies, enabling the applications of the Nanocomposites for specific recognition of biological targets.
Abstract: The synthesis of a new kind of magnetic, fluorescent multifunctional nanoparticles (approximately 30 nm in diameter) was demonstrated, where multiple fluorescent CdTe quantum dots (QDs) are covalently linked to and assembled around individual silica-coated superparamagnetic Fe(3)O(4) nanoparticles and active carboxylic groups are presented on the surface for easy bioconjugation with biomolecules. The Fe(3)O(4) nanoparticles were first functionalized with thiol groups, followed by chemical conjugation with multiple thioglycolic acid modified CdTe QDs to form water-soluble Fe(3)O(4)/CdTe magnetic/fluorescent nanocomposites. X-ray diffraction, infrared spectroscopy, transmission electron microscopy, absorption and fluorescence spectroscopy, and magnetometry were applied to fully characterize the multifunctional nanocomposites. The nanocomposites were found to exhibit magnetic and fluorescent properties favorable for their applications in magnetic separation and guiding as well as fluorescent imaging. The carboxyl groups on the nanocomposite surface were proved to be chemically active and readily available for further bioconjugation with biomolecules such as bovine serum albumin and antibodies, enabling the applications of the nanocomposites for specific recognition of biological targets. The Fe(3)O(4)/CdTe magnetic/fluorescent nanocomposites conjugated with anti-CEACAM8 antibody were successfully employed for immuno-labeling and fluorescent imaging of HeLa cells.
TL;DR: The Fe(3)O(4)/NaYF(4) : Yb,Er magnetic/luminescent nanocomposites were successfully conjugated with a protein called transferrin, which specifically recognizes the transferrin receptors overexpressed on HeLa cells, and can be employed for biolabeling and fluorescent imaging of He La cells.
Abstract: A new kind of magnetic/luminescent multifunctional nanoparticles was synthesized by covalently linking multiple carboxyl-functionalized superparamagnetic Fe(3)O(4) nanoparticles and individual amino-functionalized silica-coated fluorescent NaYF(4) : Yb,Er up-conversion nanoparticles (UCNPs). The resultant nanocomposites bear active carboxylic and amino groups on the surface that were proved to be chemically active and useful for further facile bioconjugation with biomolecules. The UCNPs in the nanocomposite particles can emit visible light in response to the irradiation by near infrared (NIR) light, enabling the application of the nanocomposites in bioimaging. X-Ray diffraction, infrared spectroscopy, transmission electron microscopy, luminescence spectroscopy, and magnetometry were applied to characterize the multifunctional nanocomposites. The nanocomposites exhibited good superparamagnetic and excellent green up-conversion photoluminescent properties that can be exploited in magnetic separation and guiding as well as bioimaging. Due to the presence of active functional groups on the nanocomposite surface, the Fe(3)O(4)/NaYF(4) : Yb,Er magnetic/luminescent nanocomposites were successfully conjugated with a protein called transferrin, which specifically recognizes the transferrin receptors overexpressed on HeLa cells, and can be employed for biolabeling and fluorescent imaging of HeLa cells. Because NIR light can penetrate biological samples with good depth without damaging them and can avoid autofluorescence from them, the presence of both NIR-responsive UCNPs and superparamagnetic nanoparticles in the nanocomposite particles will enable the practical application of the nanocomposites in bioimaging and separation.
01 Dec 2010
TL;DR: Using the Kronecker product technique and the stochastic Lyapunov method, a delay-dependent sufficient criterion of stochastics stability is obtained for the closed-loop CDES, which also guarantees that the CDNs are stochastically synchronized.
Abstract: This paper is concerned with the networked synchronization control problem of coupled dynamic networks (CDNs) with time-varying delay. First, both the data packet dropouts and network-induced delays are taken into account in the synchronization controller design. A Markovian jump process is induced to describe the packet dropouts. The network-induced delays are interval time varying and depend on the Markovian jump modes. A new closed-loop coupled dynamic error system (CDES) with Markovian jump parameters and interval time-varying delays is constructed. Second, using the Kronecker product technique and the stochastic Lyapunov method, a delay-dependent sufficient criterion of stochastic stability is obtained for the closed-loop CDES, which also guarantees that the CDNs are stochastically synchronized. Finally, a simulation example is given to demonstrate the effectiveness of the proposed result.
TL;DR: A new hybrid artificial intelligent technique called ensemble ELM is developed for regression problem and can improved generalization performance and boost the accuracy, and the accuracy of the temperature prediction is satisfied for the process of practical producing.
Abstract: Combined the modified AdaBoost.RT with extreme learning machine (ELM), a new hybrid artificial intelligent technique called ensemble ELM is developed for regression problem in this study. First, a new ELM algorithm is selected as ensemble predictor due to its rapid speed and good performance. Second, a modified AdaBoost.RT is proposed to overcome the limitation of original AdaBoost.RT by self-adaptively modifying the threshold value. Then, an ensemble ELM is presented by using the modified AdaBoost.RT for better accuracy of predictability than individual method. Finally, this new hybrid intelligence method is used to establish a temperature prediction model of molten steel by analyzing the metallurgic process of ladle furnace (LF). The model is examined by data of production from 300t LF in Baoshan Iron and Steel Co., Ltd. and compared with the models that established by single ELM, GA-BP (combined genetic algorithm with BP network), and original AdaBoost.RT. The experiments demonstrated that the hybrid intelligence method can improved generalization performance and boost the accuracy, and the accuracy of the temperature prediction is satisfied for the process of practical producing.
TL;DR: Based on a large number of experiments, the NGHS has demonstrated stronger convergence and stability than original harmony search (HS) algorithm and its two improved algorithms (IHS and SGHS).
05 Jul 2010
TL;DR: A model system in which cloud computing system is combined with trusted computing platform with trusted platform module is proposed, in which some important security services, including authentication, confidentiality and integrity, are provided in cloud Computing system.
Abstract: Cloud computing provides people the way to share distributed resources and services that belong to different organizations or sites. Since cloud computing share distributed resources via the network in the open environment, thus it makes security problems important for us to develop the cloud computing application. In this paper, we pay attention to the security requirements in cloud computing environment. We proposed a method to build a trusted computing environment for cloud computing system by integrating the trusted computing platform into cloud computing system. We propose a model system in which cloud computing system is combined with trusted computing platform with trusted platform module. In this model, some important security services, including authentication, confidentiality and integrity, are provided in cloud computing system.
01 Dec 2010
TL;DR: A new PSO model, called PSO with composite particles (PSO-CP), to address dynamic optimization problems, which partitions the swarm into a set of composite particles based on their similarity using a “worst first” principle.
Abstract: In recent years, there has been a growing interest in the study of particle swarm optimization (PSO) in dynamic environments. This paper presents a new PSO model, called PSO with composite particles (PSO-CP), to address dynamic optimization problems. PSO-CP partitions the swarm into a set of composite particles based on their similarity using a “worst first” principle. Inspired by the composite particle phenomenon in physics, the elementary members in each composite particle interact via a velocity-anisotropic reflection scheme to integrate valuable information for effectively and rapidly finding the promising optima in the search space. Each composite particle maintains the diversity by a scattering operator. In addition, an integral movement strategy is introduced to promote the swarm diversity. Experiments on a typical dynamic test benchmark problem provide a guideline for setting the involved parameters and show that PSO-CP is efficient in comparison with several state-of-the-art PSO algorithms for dynamic optimization problems.
TL;DR: The droplet in a microfluidics system can be seen as an isolated reactor, with low consumption of samples and reagents, minimal dispersion and flexible control.
Abstract: Since the start of micro total analysis systems, manipulation of droplets in a microfluidic channel has been one of the most important branches of microfluidics The scale of the droplet is remarkably small so that its mixing and reaction are rapid With an array of channels and reliable programming, droplet microfluidics provides a high-throughput platform for applications in chemistry and biology The droplet in a microfluidics system can be seen as an isolated reactor, with low consumption of samples and reagents, minimal dispersion and flexible control We review progress in manipulation of droplets in microfluidic systems and their applications We also discuss future perspectives
TL;DR: The existence of an MPB in a ferromagnetic system TbCo2-DyCo2 is reported, which demonstrates a 3-6 times larger "figure of merit" of magnetostrictive response compared with that of the off-MPB compositions.
Abstract: For more than half of a century, morphotropic phase boundary (MPB) in ferroelectric materials has drawn constant interest because it can significantly enhance the piezoelectric properties. However, MPB has been studied merely in ferroelectric systems, not in another large class of ferroic systems, the ferromagnets. In this Letter, we report the existence of an MPB in a ferromagnetic system TbCo2-DyCo2. Such a magnetic MPB involves a first-order magnetoelastic transition, at which both magnetization direction and crystal structure change simultaneously. The MPB composition demonstrates a 3-6 times larger "figure of merit" of magnetostrictive response compared with that of the off-MPB compositions. The finding of MPB in ferromagnets may help to discover novel high-performance magnetostrictive and even magnetoelectric materials.
TL;DR: A sufficient condition on the existence of such a scheduling policy is presented for a collection of networked LTI systems with sampled-data controllers and uncertain network-induced delays, and a scheduling-and-feedback-control codesign procedure is proposed.
Abstract: This paper is concerned with the simultaneous stabilization of a collection of continuous-time linear time-invariant (LTI) plants whose feedback-control loops are closed via a shared digital communication network. Because of the limitation of communication capacity, only a limited number of controller-plant connections can be accommodated at any time instant. Therefore, it is necessary to carefully determine a scheduling policy so as to achieve a simultaneous stabilization for all these control loops. A sufficient condition on the existence of such a scheduling policy is presented for a collection of networked LTI systems with sampled-data controllers and uncertain network-induced delays. The proof for the schedulability condition is in a constructive way, which can also serve as a systematic method to design a scheduling policy. Finally, a scheduling-and-feedback-control codesign procedure is proposed for the simultaneous stabilization of the collection of networked LTI systems, and the effectiveness of the proposed codesign procedure is demonstrated with simulation results.
TL;DR: The linear matrix inequality (LMI) method is applied to propose some new sufficient stability conditions for the reaction-diffusion Cohen-Grossberg neural networks with continuously distributed delays to improve upon the existing stability results.
Abstract: This paper is concerned with the global asymptotic stability of a class of reaction-diffusion Cohen-Grossberg neural networks with continuously distributed delays. Under some suitable assumptions and using a matrix decomposition method, we apply the linear matrix inequality (LMI) method to propose some new sufficient stability conditions for the reaction-diffusion Cohen-Grossberg neural networks with continuously distributed delays. The obtained results are easy to check and improve upon the existing stability results. Some remarks are given to show the advantages of the obtained results over the previous results. An example is also given to demonstrate the effectiveness of the obtained results.
TL;DR: The multi-granularity uncertain linguistic term is a form of uncertain preference information in group decision-making (GDM), while it is seldom discussed in the existing research, and a method is proposed to solve the GDM problem with multi- granular uncertain linguistic information.
Abstract: The multi-granularity uncertain linguistic term is a form of uncertain preference information in group decision-making (GDM), while it is seldom discussed in the existing research. In this paper, a method is proposed to solve the GDM problem with multi-granularity uncertain linguistic information. Firstly, to process multi-granularity uncertain linguistic information, a formula for transforming multi-granularity uncertain linguistic terms into trapezoidal fuzzy numbers is given based on the theoretical analysis. Thus, the GDM problem with multi-granularity uncertain linguistic information is changed into the one with fuzzy numbers. Then, to solve the GDM problem, an appropriate extension of the classical TOPSIS is conducted. Fuzzy positive-ideal solution (FPIS) and fuzzy negative-ideal solution (FNIS) are defined, respectively. The closeness coefficient is obtained to determine the ranking order of all alternatives by calculating the distances to both FPIS and FNIS, simultaneously. Finally, a numerical example is given to illustrate the use of the proposed method.
TL;DR: The proposed fuzzy-filtering method can get a better noise-attenuation performance when frequency ranges of noises are known beforehand, and can be used for discrete-time nonlinear systems in the Takagi-Sugeno (T-S) form.
Abstract: This paper is concerned with the problem of fuzzy-filter design for discrete-time nonlinear systems in the Takagi-Sugeno (T-S) form. Different from existing fuzzy filters, the proposed ones are designed in finite-frequency domain. First, a so-called finite-frequency l2 gain is defined that extends the standard l2 gain. Then, a sufficient condition for the filtering-error system with a finite-frequency l2 gain is derived. Based on the obtained condition, three fuzzy filters are designed to deal with noises in the low-, middle-, and high-frequency domain, respectively. The proposed fuzzy-filtering method can get a better noise-attenuation performance when frequency ranges of noises are known beforehand. An example about a tunnel-diode circuit is given to illustrate its effectiveness.
TL;DR: In this paper, single-wall carbon nanohorns (SWCNHs) with different morphologies were generated by direct current arc-discharge between pure graphite rods in different atmospheres, including air, CO2 and CO.
30 Nov 2010
TL;DR: A tool for multicore timing analysis is developed that allows automatic generation of the TA models from binary code and WCET estimation for any given TA model of the shared bus, and the combined approach can significantly tighten the estimations.
Abstract: It is predicted that multicores will be increasingly used in future embedded real-time systems for high performance and low energy consumption. The major obstacle is that we may not predict and provide any guarantee on real-time properties of software on such platforms. The shared memory bus is among the most critical resources, which severely degrade the timing predictability of multicore software due to the access contention between cores. In this paper, we study a multicore architecture where each core has a local L1 cache and all cores use a shared bus to access the off-chip memory. We use Abstract Interpretation (AI) to analyze the local cache behavior of a program running on a dedicated core. Based on the cache analysis, we construct a Timed Automaton (TA) to model when the programs access the memory bus. Then we model the shared bus also using timed automata. The TA models for the bus and programs will be explored using the UPPAAL model checker to find the WECTs for the respective programs. Based on the presented techniques, we have developed a tool for multicore timing analysis, which allows automatic generation of the TA models from binary code and WCET estimation for any given TA model of the shared bus. Extensive experiments have been conducted, showing that the combined approach can significantly tighten the estimations. As examples, we have studied the TDMA and FCFS buses, of which the WCET bounds can be tightened by up to 240% and 82% respectively, compared with the worst-case bounds estimated based on worst-case bus access delay.
TL;DR: In this paper, a new hyperchaotic system is presented by adding a nonlinear controller to the three-dimensional autonomous chaotic system, which undergoes hyperchaos, chaos, and some different periodic orbits with control parameters changed.
TL;DR: Experimental results of active learning for word sense disambiguation and text classification tasks using six real-world evaluation data sets demonstrate the effectiveness of the proposed methods, sampling by uncertainty and density (SUD) and density-based re-ranking.
Abstract: To solve the knowledge bottleneck problem, active learning has been widely used for its ability to automatically select the most informative unlabeled examples for human annotation. One of the key enabling techniques of active learning is uncertainty sampling, which uses one classifier to identify unlabeled examples with the least confidence. Uncertainty sampling often presents problems when outliers are selected. To solve the outlier problem, this paper presents two techniques, sampling by uncertainty and density (SUD) and density-based re-ranking. Both techniques prefer not only the most informative example in terms of uncertainty criterion, but also the most representative example in terms of density criterion. Experimental results of active learning for word sense disambiguation and text classification tasks using six real-world evaluation data sets demonstrate the effectiveness of the proposed methods.