New model reduction scheme for bilinear systems

A new method for the approximation of bilinear systems is proposed. The reduction scheme applies to both stable and unstable bilinear systems. The technique uses generalized input normal representations to retain the dominant part of the original system. The algorithm is evaluated on a synchronous induction generator and is shown to lead to acceptable reduced approximations of the original system. A frequency weighting is also introduced in the reduction scheme to further improve the approximation.     

H∞ decentralized fuzzy model reference tracking controldesign for nonlinear interconnected systems

In general, due to the interactions among subsystems, it is difficult to design an H_∞ decentralized controller for nonlinear interconnected systems. The model reference tracking control problem of nonlinear interconnected systems is studied via H_∞ decentralized fuzzy control method. First, the nonlinear interconnected system is represented by an equivalent Takagi-Sugeno type fuzzy model. A state feedback decentralized fuzzy control scheme is developed to override the external disturbances such that the H∞ model reference tracking performance is achieved. Furthermore, the stability of the nonlinear interconnected systems is also guaranteed. If states are not all available, a decentralized fuzzy observer is proposed to estimate the states of each subsystem for decentralized control. Consequently, a fuzzy observer-based state feedback decentralized fuzzy controller is proposed to solve the H_∞ tracking control design problem for nonlinear interconnected systems. The problem of H _∞ decentralized fuzzy tracking control design for nonlinear interconnected systems is characterized in terms of solving an eigenvalue problem (EVP). The EVP can be solved very efficiently using convex optimization techniques. Finally, simulation examples are given to illustrate the tracking performance of the proposed methods     

计算智能的新框架:生物网络结构

由神经、内分泌和免疫这3个生物系统可分别引发人工神经网络（ANN）、人工内分泌系（AES）和人工免疫系统（AIS），它们三者所组成的“神经内分泌免疫网络”在生物系统中扮演着维持动态平衡的重要角色．为此，提出一种将ANN、AES和AIS进行集成的计算智能的新框架，称之为“生物网络结构”，并讨论了其中应着重关注的研究方向和思路，从而使人们在此框架的基础上研究其理论和应用技术，更好地满足复杂系统对计算智能的应用需求．     

A computational scheme in generalized coordinates for viscous incompressible flows

This paper presents a computational scheme suitable for analyzing viscous incompressible flows in generalized curvilinear coordinate system. The scheme is based on finite volume algorithm with an overlapping staggered grid. The pseudo-diffusive terms arising from the coordinate transformation are treated as source terms. The system of nonlinear algebraic equations is solved by a semi-implicit procedure based upon line-relaxation and a generalization of Patankar's pressure correction algorithm. Examples of the application of the algorithm to flow in convergent channels, developing flow in a U-bend, and flow past backward facing step, are given. In addition, the case of flow past backward facing step is analyzed in detail, and the computed flowfields are found to be in close agreement with previous experimental and numerical results for expansion ratio (defined as the ratio of step height to channel height) of 0.5. The results are summarized in the form of a correlation relating the primary separation length, Reynolds number and expansion ratio.     

基于降阶观测器的双线性系统H∞补偿器设计

讨论了多输入多输出双线性连续时间系统的基于降阶观测器的[H∞]补偿器设计问题。利用线性矩阵不等式和Lyapunov方程,得到了保证闭环系统全局渐近稳定且满足给定干扰抑制水平的bang-bang控制律的设计。仿真验证了所给理论结果的有效性。     

A SPH-based particle model for computational microrheology

In this article we present a particle method based on smoothed particle hydrodynamics for microrheology simulations of polymeric fluids. The viscoelasticity of the solvent is modelled via a standard Oldroyd-B model and thermal fluctuations, inherently present at the microscopic scale, are incorporated into the particle framework by application of the GENERIC formalism, ensuring the strict fulfilment of the Fluctuation?CDissipation theorem at the discrete level. Rigid structures of arbitrary shape suspended in the viscoelastic solvent are modelled by freezing SPH particles within a given solid domain and letting them interact with the solvent particles. The rheological properties of the Oldroyd-B fluid, namely frequency-dependent storage and loss moduli, are extracted via macroscopic deterministic simulations under small amplitude oscillatory (SAOS) flow and, alternatively, through standard microrheological simulations of a probe particle suspended in the same Brownian viscoelastic medium, by assuming the validity of a generalized Stokes?CEinstein relation (GSER). We check that good agreement with the analytical theory for the Oldroyd-B model is found in the deterministic SAOS flow over the entire regime of frequencies investigated. Concerning the microrheological measurements, good agreement is observed only up to a maximal frequencies corresponding to time scales considerably larger than the viscous time of the probe particle where the diffusive regime is fully established. At larger investigated frequencies, a crossover between diffusive and ballistic behaviour for the MSD of the probe is observed and validity of the GSER is questionable. The model presented here provides an optimal computational framework to complement experimental observations and to analyse quantitatively the basic assumptions involved in the theory of microrheology.     

A computational model for visual selection.

We propose a computational model for detecting and localizing instances from an object class in static gray-level images. We divide detection into visual selection and final classification, concentrating on the former: drastically reducing the number of candidate regions that require further, usually more intensive, processing, but with a minimum of computation and missed detections. Bottom-up processing is based on local groupings of edge fragments constrained by loose geometrical relationships. They have no a priori semantic or geometric interpretation. The role of training is to select special groupings that are moderately likely at certain places on the object but rate in the background. We show that the statistics in both populations are stable. The candidate regions are those that contain global arrangements of several local groupings. Whereas our model was not conceived to explain brain functions, it does cohere with evidence about the functions of neurons in V1 and V2, such as responses to coarse or incomplete patterns (e.g., illusory contours) and to scale and translation invariance in IT. Finally, the algorithm is applied to face and symbol detection.     

A parallel computational model for GATE simulations

GATE/Geant4 Monte Carlo simulations are computationally demanding applications, requiring thousands of processor hours to produce realistic results. The classical strategy of distributing the simulation of individual events does not apply efficiently for Positron Emission Tomography (PET) experiments, because it requires a centralized coincidence processing and large communication overheads. We propose a parallel computational model for GATE that handles event generation and coincidence processing in a simple and efficient way by decentralizing event generation and processing but maintaining a centralized event and time coordinator. The model is implemented with the inclusion of a new set of factory classes that can run the same executable in sequential or parallel mode. A Mann–Whitney test shows that the output produced by this parallel model in terms of number of tallies is equivalent (but not equal) to its sequential counterpart. Computational performance evaluation shows that the software is scalable and well balanced.     

A computational reinforced learning scheme to blind imagedeconvolution

This paper presents a new approach to adaptive blind image deconvolution based on computational reinforced learning in an attractor-embedded solution space. The new technique develops an evolutionary strategy that generates the improved blur and image populations progressively. A dynamic attractor space is constructed by integrating the knowledge domain of the blur structures into the algorithm. The attractors are predicted using a maximum a posteriori estimator and their relevance is evaluated with respect to the computed blurs. We develop a novel reinforced mutation scheme that combines stochastic search and pattern acquisition throughout the blur identification. It enhances the algorithmic convergence and reduces the computational cost significantly. The new technique is robust in alleviating the constraints and difficulties encountered by most conventional methods. Experimental results show that the new algorithm is effective in restoring the degraded images and identifying the blurs     

ASHSD-II: A computational model for litigation support

Abstract: In this paper a hybrid knowledge-based system which exploits both rule-based reasoning (RBR) and case-based reasoning (CBR) is presented. The issues of RBR and CBR in general in the context of legal knowledge-based systems and legislation in rule form and previously-decided cases in an interconnected graph form are discussed. It is possible for the user to select either reasoning method (RBR or CBR), or indicate no preference. The rule base of this system consists of two types of rule. The first type of rule determines which options are legally applicable. The second type indicates how the courts are likely to act within the range of options available, which is determined by the first type of rule. When CBR is selected, the system uses the features of previously-decided cases to select the most similar cases to the situation that is described in the input and displays their details of decisions. In case of the selection of no preference option, the system applies RBR and CBR method separately, and then presents results based on an automated relative rating of the qualities of the RBR (based on the second type of rules) and CBR advice. These ideas have been implemented in a prototype system, known as A dvisory S upport for H ome S ettlement in D ivorce (ASHSD-II).     

A computational model for static data flow machines

Computer architects have been constantly looking for new approaches to design high-performance machines. Data flow and VLSI offer two mutually supportive approaches towards a promising design for future super-computers. When very high speed computations are needed, data flow machines may be relied upon as an adequate solution in which extremely parallel processing is achieved.

This paper presents a formal analysis for data flow machines. Moreover, the following three machines are considered: (1) MIT static data flow machine; (2) TI's DDP static data flow machine; (3) LAU data flow machine.

These machines are investigated by making use of a reference model. The contributions of this paper include: (1) Developing a Data Flow Random Access Machine model (DFRAM), for first time, to serve as a formal modeling tool. Also, by making use of this model one can calculate the time cost of various static data machines, as well as the performance of these machines. (2) Constructing a practical Data Flow Simulator (DFS) on the basis of the DFRAM model. Such DFS is modular and portable and can be implemented with less sophistication. The DFS is used not only to study the performance of the underlying data flow machines but also to verify the DFRAM model.     

Security‐aware scheduling model for computational grid

Grid applications with stringent security requirements introduce challenging concerns because the schedule devised by nonsecurity‐aware scheduling algorithms may suffer in scheduling security constraints tasks. To make security‐aware scheduling, estimation and quantification of security overhead is necessary. The proposed model quantifies security, in the form of security levels, on the basis of the negotiated cipher suite between task and the grid‐node and incorporates it into existing heuristics MinMin and MaxMin to make it security‐aware MinMin(SA) and MaxMin(SA). It also proposes SPMaxMin (Security Prioritized MinMin) and its comparison with three heuristics MinMin(SA), MaxMin(SA), and SPMinMin on heterogeneous grid/task environment. Extensive computer simulation results reveal that the performance of the various heuristics varies with the variation in computational and security heterogeneity. Its analysis over nine heterogeneous grid/task workload situations indicates that an algorithm that performs better for one workload degrades in another. It is conspicuous that for a particular workload one algorithm gives better makespan while another gives better response time. Finally, a security‐aware scheduling model is proposed, which adapts itself to the dynamic nature of the grid and picks the best suited algorithm among the four analyzed heuristics on the basis of job characteristics, grid characteristics, and desired performance metric. Copyright © 2011 John Wiley & Sons, Ltd.     

A computational model for RNA multiple structural alignment

This paper addresses the problem of aligning multiple sequences of noncoding RNA (ncRNA) genes. We approach this problem with the biologically motivated paradigm that scoring of ncRNA alignments should be based primarily on secondary structure rather than nucleotide conservation. We introduce a novel graph theoretic model (NLG) for analyzing algorithms based on this approach, prove that the RNA multiple alignment problem is NP-Complete in this model, and present a polynomial time algorithm that approximates the optimal structure of size S   within a factor of O(log²S)$\mathrm{O}({\log }^{2}S)$.     

A single-program-multiple-data computational model for EPEX/FORTRAN

We present a single-program-multiple-data computational model which we have implemented in the EPEX system to run in parallel mode FORTRAN scientific application programs. The computational model assumes a shared memory organization and is based on the scheme that all processes executing a program in parallel remain in existence for the entire execution; however, the tasks to be executed by each process are determined dynamically during execution by the use of appropriate synchronizing constructs that are imbedded in the program. We have demonstrated the applicability of the model in the parallelization of several applications. We discuss parallelization features of these applications and performance issues such as overhead, speedup, efficiency.     

A computational model for tiling recognizable two-dimensional languages

Tiling systems are a well accepted model to define recognizable two-dimensional languages but they are not an effective device for recognition unless a scanning strategy for the pictures is fixed. We define a tiling automaton as a tiling system equipped with a scanning strategy and a suitable data structure. The class of languages accepted by tiling automata coincides with the REC family. In this framework it is possible to define determinism, non-determinism and unambiguity. Then (deterministic) tiling automata are compared with the other known (deterministic) automata models for two-dimensional languages.     

A computational knowledge representation model for cognitive computers

The accumulating data are easy to store but the ability of understanding and using it does not keep track with its growth. So researches focus on the nature of knowledge processing in the mind. This paper proposes a semantic model (CKRMCC) based on cognitive aspects that enables cognitive computer to process the knowledge as the human mind and find a suitable representation of that knowledge. In cognitive computer, knowledge processing passes through three major stages: knowledge acquisition and encoding, knowledge representation, and knowledge inference and validation. The core of CKRMCC is knowledge representation, which in turn proceeds through four phases: prototype formation phase, discrimination phase, generalization phase, and algorithm development phase. Each of those phases is mathematically formulated using the notions of real-time process algebra. The performance efficiency of CKRMCC is evaluated using some datasets from the well-known UCI repository of machine learning datasets. The acquired datasets are divided into training and testing data that are encoded using concept matrix. Consequently, in the knowledge representation stage, a set of symbolic rule is derived to establish a suitable representation for the training datasets. This representation will be available in a usable form when it is needed in the future. The inference stage uses the rule set to obtain the classes of the encoded testing datasets. Finally, knowledge validation phase is validating and verifying the results of applying the rule set on testing datasets. The performances are compared with classification and regression tree and support vector machine and prove that CKRMCC has an efficient performance in representing the knowledge using symbolic rules.     

A computational model of coordination

This paper explores and explicates the concept of coordination in small workgroups. The authors develop a computational model within which the key constructs of coordination can be precisely defined. This framework can serve as the basis for additional theoretical developments and as a basis for developing software systems designed to support coordination in workgroups. Coordination is viewed as management of conflicting demands on finite resources by actors or activities. These activities may be informational in nature. The authors conceptualize the underlying primitive concepts that constitute the idea of coordination. This conceptualization views coordination as a cycle of four phases: the definitional phase, the conflict resolution phase, the action phase, and the adaptation phase. The authors then set forth a formal model of coordination, both as a phenomenon and as a process. Within the context of the framework the authors have created, key concepts of decomposability, Pareto-satisfactory, informational privacy, informational decentralization, and informational efficiency are explored     

H∞-output feedback controller design for linear systems withtime-varying delayed state

This paper considers the H∞-controller design problem for linear systems with time-varying delays in states. The authors obtain sufficient conditions for the existence of H∞ controllers in terms of three linear matrix inequalities (LMIs). These sufficient conditions are dependent on the maximum value of the time derivative of time-varying delay. Furthermore, they briefly explain how to construct such controllers from the positive-definite solutions of their LMIs and give an example