A frequency domain approach to state estimation

A novel input-to-state approach to the problem of robust state estimation in the presence of model uncertainty as well as plant disturbance and sensor noise is considered. A new observer structure is introduced and shown to have certain advantages over the classical Luenberger observer when studying robustness issues. This structure is then used to solve the robust estimation problem in the H_∞ framework.     

A maximum principle approach to optimal control for one-dimensional hyperbolic systems with several state variables

The maximum principle developed by Sloss et al. [Optimal control of structural dynamic systems in one space dimension using a maximum principle, J. Vibr. Control 11 (2005) 245–261] is used to determine the optimal control functions for a class of one-dimensional distributed parameter structures. The distributed parameter structures are governed by systems of fourth order hyperbolic equations with constant coefficients. A quadratic performance index is formulated as the cost functional of the problem and can be used to represent the energy of the structure and the force spent in the control process. The developed maximum principle establishes a theoretical foundation for the solution of the optimal control problem and relates the optimal control vector to an adjoint variable vector. The method of solution is outlined which involves reducing the original problem to a system of ordinary differential equations. The solution of the general problem is given and a structural control problem is solved to illustrate the solution procedure. The effectiveness of the proposed control solution is shown by comparing the behavior of controlled and uncontrolled systems.     

A novel approach for robust model predictive control applied to switched linear systems through state and output feedback

This paper proposes a robust switched model-based predictive controller design for discrete linear systems with state constraints, inputs, and disturbances limited in norm. Modeled via linear matrix inequalities, the online and offline designs of the proposed control aim at minimizing the upper bound of the quadratic performance index for a horizon of infinite prediction associated with the state estimator and the switching rule, seeking to guarantee the robust stability for closed-loop systems. To this end, three theorems are formulated. To demonstrate the effectiveness of the control strategy, a comparative analysis is performed between the performance of the proposed model and a benchmark method. From the results, it is possible to conclude that the proposed method is promising in the scope of control of linear systems subject to switching, being more efficient than the benchmark for the stabilization and control of both numerical examples.     

A socio-cognitive approach to innovation

This paper argues that case studies in innovation research at the level of the firm require an explicit model of how people think and act in organisations. The ‘socio-cognitive’ approach which is outlined here combines Weick's social psychological ideas with Teece's characterisation of the firm by its technological knowledge base. It is argued that a cognitive approach accommodates the tacit nature of technological knowledge well, and that artefacts can be considered as cognitive resources. A distinction is made between the social control of production technology (the firm) and of user technology (typically the market). This distinction is used as the basis for conceptualising technological innovation as the creation by individuals within the firm of ‘cognitive ensembles’ composed of cognitive elements drawn from both the technological base of the firm and market conceptions.This focus on the process by which innovation is generated within the firm leads to a brief discussion of previous work on the creative process and the relevance of the socio-cognitive approach to macro-level studies of technological change.     

Combined state and parameter estimation for a bilinear state space system with moving average noise

This paper considers the identification problem of bilinear systems with measurement noise in the form of the moving average model. In particular, we present an interactive estimation algorithm for unmeasurable states and parameters based on the hierarchical identification principle. For unknown states, we formulate a novel bilinear state observer from input-output measurements using the Kalman filter. Then a bilinear state observer based multi-innovation extended stochastic gradient (BSO-MI-ESG) algorithm is proposed to estimate the unknown system parameters. A linear filter is utilized to improve the parameter estimation accuracy and a filtering based BSO-MI-ESG algorithm is presented using the data filtering technique. In the numerical example, we illustrate the effectiveness of the proposed identification methods.     

Beyond vector space model for hierarchical Arabic text classification: A Markov chain approach

The vector space model (VSM) is a textual representation method that is widely used in documents classification. However, it remains to be a space-challenging problem. One attempt to alleviate the space problem is by using dimensionality reduction techniques, however, such techniques have deficiencies such as losing some important information. In this paper, we propose a novel text classification method that neither uses VSM nor dimensionality reduction techniques. The proposed method is a space efficient method that utilizes the first order Markov model for hierarchical Arabic text classification. For each category and sub-category, a Markov chain model is prepared based on the neighboring characters sequences. The prepared models are then used for scoring documents for classification purposes. For evaluation, we used a hierarchical Arabic text data collection that contains 11,191 documents that belong to eight topics distributed into 3-levels. The experimental results show that the Markov chains based method significantly outperforms the baseline system that employs the latent semantic indexing (LSI) method. That is, the proposed method enhances the F1-measure by 3.47%. The novelty of this work lies on the idea of decomposing words into sequences of characters, which found to be a promising approach in terms of space and accuracy. Based on our best knowledge, this is the first attempt to conduct research for hierarchical Arabic text classification with such relatively large data collection.     

A cognitive approach to science policy

Recent work in the social studies of science has emphasized the importance of studying both the social and cognitive aspects of the evolution of scientific specialties and disciplines. This has implications for science policies that aim at the direction of scientific fields toward external goals: the cognitive state and dynamics of the field have to be taken into acount. Such a cognitive approach to science policy has been elaborated by a number of German science scholars. The three-phase model of scientific developments and the finalization thesis of the Starnberg group is discussed, and the policy implications are critically reviewed. A group based in the University of Bielefeld has published case studies designed to trace the role of cognitive factors in explaining the impact of science policy programmes on scientific fields. It turns out that mutual adaptation processes occur in the course of formulating the programmes which reduce conflict and resistance. In conclusion, some perspectives for further work are noted.     

A systems-based approach to industry classification

I propose an alternative approach to industry classification that reflects the way in which firms self-organize into industries and sectors. The systems-based approach to industry classification takes the sector as the primary unit of analysis, defines a sector on the basis of similarity in needs to which firms collectively respond, and disaggregates sectors into subsectors and industries on the basis of recursive hierarchical dependency. The result is an approach to industry classification that reflects industry structure, reduces egregious cases of between-class homogeneity and within-class heterogeneity, and accommodates changes in technology. The approach is illustrated in a communications equipment subsector application.     

Robust stabilization of polytopic discrete-time systems with time-varying state delay: A convex approach

Convex conditions, expressed as linear matrix inequalities (LMIs), for stability analysis and robust design of uncertain discrete-time systems with time-varying delay are presented in this paper. Delay-dependent and delay-independent convex conditions are given. This paper is particularly devoted to the synthesis case where convex conditions are proposed to consider maximum allowed delay interval. It is also presented some relaxed LMIs that yield less conservative conditions at the expense of increasing the computational burden. Extensions to cope with decentralized control and output feedback control are discussed. Numerical examples, including real world motivated models, are presented to illustrate the effectiveness of the proposed approach.     

Trading performance for state constraint feasibility in stochastic constrained control: A randomized approach

Constrained control for stochastic linear systems is generally a difficult task due to the possible infeasibility of state constraints. In this paper, we focus on a finite control horizon and propose a design methodology where the constrained control problem is formulated as a chance-constrained optimization problem depending on some parameter. This parameter can be tuned so as to decide the appropriate trade-off between control cost minimization and state constraints satisfaction. An approximate solution is computed via a randomized algorithm. Precise guarantees about its feasibility for the original chance-constrained problem are provided. A numerical example shows the efficacy of the proposed methodology.     

A language-based approach to measuring creative exploration

We propose a new measure of the exploratory activities of companies based on the idea that experimentation with new courses of action and the need to describe them entails the adoption of new words in firm regulatory disclosures. Unlike traditional indicators, such as R&D spending, the proposed exploration indicator is available for all publicly traded firms across all industries. The exploration indicator predicts firm knowledge accumulation, as measured by future patenting and trademarking activities. It further shows that firm exploration declines after periods of high R&D spending and over time. The exploration indicator correlates positively with firm risk and exhibits a distinct positive impact on firm value unexplained by traditional innovation indicators. Our language-based approach can be applied to measure creative contributions in other domains, such as government grant applications and academic publications.     

科学的伦理学之路：神经伦理学

 传统的超越主义伦理学进路存在着不少无法解决的问题,而近年来产生的神经伦理学为解决这些问题提供了一个新的视角。神经伦理学因立足于认知神经科学对道德判断、自由意志和元伦理学等问题的涉及而成为“科学”的伦理学,但也因其还原论倾向而对传统伦理学形成了挑战。     

A recursive approach to office object modelling

Office modelling aims at providing abstract, conceptual tools for describing office semantics in order to ease high-level and are more likely to correct application programming. Advanced office systems are complex organizations of objects, procedures, activities, locations, etc., incorporating independent user workstations with which people cooperate in executing complicated office tasks.Office information systems support the representation, storage, access and maintenance of complex data objects occurring frequently in office environments. Based on the structural aspects of such office objects, we provide language constructs to model office procedures which are perceived as being based on the following semantic primitives: complex data object constructors that accept object components (e.g., addresses, dates, text-fragments) and return office objects of composite type (e.g., letters, forms, memos), component selectors that select specific object components from composite data objects, and operations for recognizing the type of a given complex data object. We gain our language constructs by generalizing the corresponding solutions provided by conventional, record-based database models. This leads us from the ‘flat structures’ of traditional database models to ‘recursive structures’ allowing for flexible representations of non-formatted and highly related data as required for advanced office object modelling.     

A domain-independent approach to finding related entities

We propose an approach to the retrieval of entities that have a specific relationship with the entity given in a query. Our research goal is to investigate whether related entity finding problem can be addressed by combining a measure of relatedness of candidate answer entities to the query, and likelihood that the candidate answer entity belongs to the target entity category specified in the query. An initial list of candidate entities, extracted from top ranked documents retrieved for the query, is refined using a number of statistical and linguistic methods. The proposed method extracts the category of the target entity from the query, identifies instances of this category as seed entities, and computes similarity between candidate and seed entities. The evaluation was conducted on the Related Entity Finding task of the Entity Track of TREC 2010, as well as the QA list questions from TREC 2005 and 2006. Evaluation results demonstrate that the proposed methods are effective in finding related entities.     

Parameter estimation for an input nonlinear state space system with time delay

This paper researches parameter estimation problems for an input nonlinear system with state time-delay. Combining the linear transformation and the property of the shift operator, the system is transformed into a bilinear parameter identification model. A gradient based and a least squares based iterative parameter estimation algorithms are presented for identifying the state time-delay system. The simulation results confirm that the proposed two algorithms are effective and the least squares based iterative algorithm has faster convergence rates than the gradient based iterative algorithm.     

A simple approach to mathematical modelling of integer order and fractional order fuzzy PID controllers using one-dimensional input space and their experimental realization

Though over the years, mathematical modelling of fuzzy PID controllers is carried out extensively with two-dimensional and three-dimensional input spaces, the modelling is rarely attempted using one-dimensional input space. In this paper, this gap is reduced by proposing a simple approach where each of the fuzzy P, fuzzy I, and fuzzy D components is modelled using one-dimensional input space and merged to provide the complete PID action. Another speciality of the proposed approach is that it does not require any AND or OR operator for obtaining the mathematical models of individual PID components. To the best of author’s knowledge, such a modelling approach is completely new. This newly introduced idea of modelling is further extended to fractional order fuzzy PID controllers. Applicability of the proposed fuzzy controllers is delineated with four simulation examples and one real-time experimentation case study. To understand the usefulness of the proposed control schemes, performances of the newly obtained controllers are compared with the results available in literature. As the proposed controllers are model-free controllers, they can easily be implemented for other control applications also.     

基于空间网格图的低复杂度联合信源信道编译码方法

在分析和研究空间网格图和优化方法的基础上,采用约束条件方程组和权重值递推方法,优化空间网格图的权重参数,提出基于空间网格图的低复杂度联合信源信道编译码方法.仿真实验结果表明,与现存方法相比,该方法有效降低了计算复杂度,并改善了译码性能,可应用于资源受限的空间通信中的流媒体的联合编译码.