Robustification of input redundant feedback systems using robust actuator weighting in the control allocation problem

In this article, a new methodology for robust actuator weighting in the control allocation (CA) problem of input redundant feedback systems is addressed. The methodology is based on the control structural properties of the plant which were previously used for control configuration selection. Robust performance (RP) measures including H _∞ norm and structured singular value of the closed-loop system are used in this article. The capability of the approach is proven with application to lateral dynamics control of the vehicle over-actuated with front and rear steering systems. Employing the RP measures, it is concluded that the vehicle feedback control with front steering angles gives superior RP properties in comparison with the feedback loop of the rear steering angles. Based on these results, the penalty weightings in the cost function of the CA unit are determined. Simulation results based on nonlinear seven degrees of freedom vehicle handling model show that the selection of penalty weightings in the CA unit based on the RP properties of the control inputs (front and rear steering angles) improves the RP of the closed-loop.     

Exact,almost and optimal input decoupled (delayed) observers

The core of this paper deals with the construction of input-decoupled observers which seek asymptotic estimation of a desired output variable (a linear combination of state and input) of a time-invariant either continuous- or discrete-time system driven by unknown inputs and disturbances. Exact, almost, optimal (suboptimal) and constrained optimal estimation or filtering problems are formulated and studied. All the problems defined and studied here are inherently interconnected, and have a strong common thread of estimation and filtering in the face of the unknown input and external disturbance signals. They are interconnected from a variety of angles, e.g. they are motivated by one another, methods of obtaining the solvability conditions and their methods of solution rely on one another, etc. Thus, a hierarchy of problems and their solutions is built on top of one another. Some of the problems studied here are known in the literature but not in as general a form as is given here, while a majority of the problems studied here are new. A classical variation of all the above problems is also studied here by introducing an l-step delay in estimating the desired output from the measured output. The underlying philosophy throughout this work has been to study most if not all of the facets of estimation and filtering in one stretch under a single folder. Our study of all the above problems has been guided by three important perspectives: (1) obtaining the solvability conditions, both necessary and sufficient; (2) obtaining optimal performance whenever it applies; and (3) developing sound methodologies to design and construct appropriate observers or filters.     

Eigenvalue assignment by minimal state-feedback gain in LTI multivariable systems

In this article, an improved method for eigenvalue assignment via state feedback in the linear time-invariant multivariable systems is proposed. This method is based on elementary similarity operations, and involves mainly utilisation of vector companion forms, and thus is very simple and easy to implement on a digital computer. In addition to the controllable systems, the proposed method can be applied for the stabilisable ones and also systems with linearly dependent inputs. Moreover, two types of state-feedback gain matrices can be achieved by this method: (1) the numerical one, which is unique, and (2) the parametric one, in which its parameters are determined in order to achieve a gain matrix with minimum Frobenius norm. The numerical examples are presented to demonstrate the advantages of the proposed method.     

Majorisations for the eigenvectors of graph-adjacency matrices

We develop majorisation results that characterise changes in eigenvector components of a graph's adjacency matrix when its topology is changed. Specifically, for general (weighted, directed) graphs, we characterise changes in dominant eigenvector components for single- and multi-row incrementations. We also show that topology changes can be tailored to set ratios between the components of the dominant eigenvector. For more limited graph classes (specifically, undirected, and reversibly-structured ones), majorisations for components of the subdominant and other eigenvectors upon graph modifications are also obtained.     

Induced-charge electro-osmosis of polymer-containing fluid around a metallic rod

Induced-charge electro-osmotic (ICEO) flow of polymer-containing electrolyte solution around a cylindrical gold-coated stainless steel rod under AC electric field is measured by micro-particle image velocimetry (micro-PIV) for the first time. The ICEO flows as functions of the amount of non-ionic PEG (polyethylene glycol), cationic PDADMA (polydiallyldimethylammonium chloride), and anionic PVSASS (polyvinylsulfonic acid sodium salt) polymers added into the salt solution, frequency, and strength of the AC electric field are measured. The ICEO flow of polymer-containing fluid around the rod is quadrupolar with four vortices and is proportional to the square of imposed electric field. The ICEO flow velocity exponentially decreases with an increased concentration of neutral PEG. Ionic polyelectrolytes significantly increase ICEO velocities due to the enriched net charge within the induced electric double layer arising from the electrostatic interaction between the polarized rod’s surface and the charged polyelectrolytes in ionic polymer solution. In addition, polymer concentration affects the dependence of the ICEO flow on the frequency of AC electric field.     

Data-Intensive Cloud Computing: Requirements, Expectations, Challenges, and Solutions

Data-intensive systems encompass terabytes to petabytes of data. Such systems require massive storage and intensive computational power in order to execute complex queries and generate timely results. Further, the rate at which this data is being generated induces extensive challenges of data storage, linking, and processing. A data-intensive cloud provides an abstraction of high availability, usability, and efficiency to users. However, underlying this abstraction, there are stringent requirements and challenges to facilitate scalable and resourceful services through effective physical infrastructure, smart networking solutions, intelligent software tools, and useful software approaches. This paper analyzes the extensive requirements which exist in data-intensive clouds, describes various challenges related to the paradigm, and assess numerous solutions in meeting these requirements and challenges. It provides a detailed study of the solutions and analyzes their capabilities in meeting emerging needs of widespread applications.     

Synthesis a Petri net based control model for a FMS cell

Petri nets have been recognised as a high level formal and graphical specification language for modelling, analysis, and control of concurrent asynchronous distributed systems. This paper presents a PN model, synthesised by an extended version of the knitting synthesis technique. This method, as an incremental design approach, establishes the conditions under which the fundamental behavioural properties of the synthesised systems are fulfilled and preserved. That is, the synthesised models are live, bounded, and reversible (cyclic). A Petri net with the aforementioned properties is called a well-behaved Petri net system which is guaranteed to operate in a deadlock-free, stable, and cyclic fashion. Well-behaved Petri net models, synthesised using the proposed method can be compiled into control codes and implemented as real-time controllers for flexible manufacturing systems. The significance of this paper is due to the application of an extended version of knitting synthesis technique to a real life example of a flexible manufacturing system.     

AN INTERACTIVE SEARCH ASSISTANT ARCHITECTURE BASED ON INTRINSIC QUERY STREAM CHARACTERISTICS

Search engine query log mining has evolved over time to more like data stream mining due to the endless and continuous sequence of queries known as query stream. In this paper, we propose an online frequent sequence discovery (OFSD) algorithm to extract frequent phrases from within query streams, based on a new frequency rate metric, which is suitable for query stream mining. OFSD is an online, single pass, and real-time frequent sequence miner appropriate for data streams. The frequent phrases extracted by the OFSD algorithm are used to guide novice Web search engine users to complete their search queries more efficiently. YourEye, our online phrase recommender is then introduced. The advantages of YourEye compared with Google Suggest, a service powered by Google for phrase suggestion, is also described. Various characteristics of two specific Web search engine query logs are analyzed and then the query logs are used to evaluate YourEye. The experimental results confirm the significant benefit of monitoring frequent phrases within the queries instead of the whole queries because none-separable items. The number of the monitored elements substantially decreases, which results in smaller memory consumption as well as better performance. Re-ranking the retrieved pages based on past users clicks for each frequent phrase extracted by OFSD is also introduced. The preliminary results show the advantages of the proposed method compared to the similar work reported in Smyth et al.     

Term frequency with average term occurrences for textual information retrieval

In the context of information retrieval (IR) from text documents, the term weighting scheme (TWS) is a key component of the matching mechanism when using the vector space model. In this paper, we propose a new TWS that is based on computing the average term occurrences of terms in documents and it also uses a discriminative approach based on the document centroid vector to remove less significant weights from the documents. We call our approach Term Frequency With Average Term Occurrence (TF-ATO). An analysis of commonly used document collections shows that test collections are not fully judged as achieving that is expensive and maybe infeasible for large collections. A document collection being fully judged means that every document in the collection acts as a relevant document to a specific query or a group of queries. The discriminative approach used in our proposed approach is a heuristic method for improving the IR effectiveness and performance and it has the advantage of not requiring previous knowledge about relevance judgements. We compare the performance of the proposed TF-ATO to the well-known TF-IDF approach and show that using TF-ATO results in better effectiveness in both static and dynamic document collections. In addition, this paper investigates the impact that stop-words removal and our discriminative approach have on TF-IDF and TF-ATO. The results show that both, stop-words removal and the discriminative approach, have a positive effect on both term-weighting schemes. More importantly, it is shown that using the proposed discriminative approach is beneficial for improving IR effectiveness and performance with no information on the relevance judgement for the collection.     

A new fast motion estimation algorithm using fast mode decision for high-efficiency video coding standard

High-efficiency video coding is the latest standardization effort of the International Organization for Standardization and the International Telecommunication Union. This new standard adopts an exhaustive algorithm of decision based on a recursive quad-tree structured coding unit, prediction unit, and transform unit. Consequently, an important coding efficiency may be achieved. However, a significant computational complexity is resulted. To speed up the encoding process, efficient algorithms based on fast mode decision and optimized motion estimation were adopted in this paper. The aim was to reduce the complexity of the motion estimation algorithm by modifying its search pattern. Then, it was combined with a new fast mode decision algorithm to further improve the coding efficiency. Experimental results show a significant speedup in terms of encoding time and bit-rate saving with tolerable quality degradation. In fact, the proposed algorithm permits a main reduction that can reach up to 75 % in encoding time. This improvement is accompanied with an average PSNR loss of 0.12 dB and a decrease by 0.5 % in terms of bit-rate.