Intelligent data analysis applied to debug complex software systems

The emergent behavior of complex systems, which arises from the interaction of multiple entities, can be difficult to validate, especially when the number of entities or their relationships grows. This validation requires understanding of what happens inside the system. In the case of multi-agent systems, which are complex systems as well, this understanding requires analyzing and interpreting execution traces containing agent specific information, deducing how the entities relate to each other, guessing which acquaintances are being built, and how the total amount of data can be interpreted. The paper introduces some techniques which have been applied in developments made with an agent oriented methodology, INGENIAS, which provides a framework for modeling complex agent oriented systems. These techniques can be regarded as intelligent data analysis techniques, all of which are oriented towards providing simplified representations of the system. These techniques range from raw data visualization to clustering and extraction of association rules.     

LPV controller design for the nonlinear RTAC system

This paper proposes three kinds of state‐feedback controllers for the nonlinear rotational and translational actuator (RTAC) benchmark problem. Interpolating a nonlinear system into an LPV system associated with time‐varying and state‐dependent weighting functions allows one to apply various linear system theories and techniques for analysing and synthesizing nonlinear systems, and thus to generate LMI criteria to supply various stabilizers and guaranteed cost controllers. This approach is more flexible for this benchmark problem than existing approaches in the literature because the other controllers excessively depend on the structure of the nonlinear system. Simulation results show the performance of the resulting controllers. Copyright © 2001 John Wiley & Sons, Ltd.     

A hybrid genetic tabu search algorithm for solving job shop scheduling problems: a case study

In recent decades many attempts have been made at the solution of Job Shop Scheduling Problem using a varied range of tools and techniques such as Branch and Bound at one end of the spectrum and Heuristics at the other end. However, the literature reviews suggest that none of these techniques are sufficient on their own to solve this stubborn NP-hard problem. Hence, it is postulated that a suitable solution method will have to exploit the key features of several strategies. We present here one such solution method incorporating Genetic Algorithm and Tabu Search. The rationale behind using such a hybrid method as in the case of other systems which use GA and TS is to combine the diversified global search and intensified local search capabilities of GA and TS respectively. The hybrid model proposed here surpasses most similar systems in solving many more traditional benchmark problems and real-life problems. This, the system achieves by the combined impact of several small but important features such as powerful chromosome representation, effective genetic operators, restricted neighbourhood strategies and efficient search strategies along with innovative initial solutions. These features combined with the hybrid strategy employed enabled the system to solve several benchmark problems optimally, which has been discussed elsewhere in Meeran and Morshed (8th Asia Pacific industrial engineering and management science conference, Kaohsiung, Taiwan, 2007). In this paper we bring out the system’s practical usage aspect and demonstrate that the system is equally capable of solving real life Job Shop problems.     

A new orthogonal series approach to state-space analysis and identification

The contribution of this paper is two-fold. First, it introduces a new orthogonal series approach to the state-space analysis of linear time-invariant systems. This approach yields explicit expressions for the state and output vector coefficient matrices. These expressions only involve the multiplication of matrices of small dimensions. No algebraic system of equations needs to be solved, and therefore no inversion of large matrices is required here, as compared to other known techniques. The second contribution consists of using this new orthogonal series technique to solve the state-space identification problem. It is shown that by appropriately manipulating the aforementioned state-space analysis results, an algorithm is derived which yields the state-space system matrix A. This algorithm gives a new outlook and a better insight into the state-space identification problem.     

A new theorem of the alternative – a location approach

This paper provides a new method to obtain theorems of the alternative, using a location approach. This consists of associating a location problem with the original system of linear inequalities. The paper introduces a dual optimization problem and strong duality is established. From the dual problem, there is derived a second system of equations and inequalities and for both systems the duality and solvability properties provide a conclusion of the general theorem of the alternative. The assigned location problem includes weighting factors and powers of the norms. These weighting factors and exponents may be considered as parameters appearing in the theorem of the alternative, which therefore represents a whole class of theorems of the alternative. The presented theorem of the alternative may be considered as a generalization of the well-known Gale's theorem of the alternative for linear inequalities.     

Optimal choice of weighting factors in adaptive linear quadratic control

Most of the processes in the industry have nonlinear behavior. Control of such processes with conventional control methods could lead to unstable, suboptimal, etc., results. On the other hand, the adaptive control is a technique widely used for controlling of nonlinear systems. The approach here is based on the recursive identification of the external linear model as a linear representation of the originally nonlinear system. The controller then reacts to the change of the working point or disturbances which could occur by the change of the parameters, structure, etc. The polynomial synthesis together with the linear quadratic(LQ) approach is employed here for the controller synthesis. These techniques satisfy basic control requirements such as the stability, the reference signal tracking and the disturbance attenuation. Resulted controller could be tuned with the choice of weighting factors in LQ approach. This work investigates the effect of these factors on control results. Proposed methods are tested on the mathematical model of the isothermal continuous stirred-tank reactor and simulated results are also verified on the real model of the continuous stirred tank reactor.     

Synthesis of 165-1165-1165-1-optimal linear feedback systems

TheH ^∞-optimization approach to feedback system design was introduced in the context of sensitivity minimization. This problem formulation yielded improper optimal controllers and did not incorporate design issues related to robustness and plant input cost. A more realistic problem formulation incorporating a weighting on the plant input was suggested earlier by the author and is pursued further in this paper. The optimization problem which results from this problem formulation is of mixed-sensitivity minimization and is solved by using tools from interpolation and approximation theory. This synthesis approach is believed to be of considerable promise for the design of linear feedback systems.     

Fault detection for output feedback control systems with actuator stuck faults: A steady‐state‐based approach

The paper studies the fault detection problem for output feedback control systems with bounded disturbances and nonzero constant reference inputs. A steady‐state‐based approach is proposed which can be used to detect small actuator stuck faults including actuator outage (the stuck value is zero). These small stuck faults, especially the outage faults, cannot be detected effectively using the existing techniques. A dynamic output feedback controller and a weighting matrix are designed simultaneously. The dynamic output feedback controller stabilizes the closed‐loop system for both fault‐free and faulty cases and attenuates the effects of disturbances. By manipulating the steady‐state values of system states with the detection weighting matrix, a residual is then generated, through which actuator stuck faults including actuator outages can be detected effectively. Simulation results are included to demonstrate our design procedure. Copyright © 2009 John Wiley & Sons, Ltd.     

Robust stability and stabilizing controller design of fuzzy systems with discrete and distributed delays

In this paper, we consider delay-dependent stability conditions of Takagi-Sugeno fuzzy systems with discrete and distributed delays. Although many kinds of stability conditions for fuzzy systems with discrete delays have already been obtained, almost no stability condition for fuzzy systems with distributed delays has appeared in the literature. This is also true in case of the robust stability for uncertain fuzzy systems with distributed delays. Here we employ a generalized Lyapunov functional to obtain delay-dependent stability conditions of fuzzy systems with discrete and distributed delays. We introduce some free weighting matrices to such a Lyapunov functional in order to reduce the conservatism in stability conditions. These techniques lead to generalized and less conservative stability conditions. We also consider the robust stability of fuzzy time-delay systems with uncertain parameters. Applying the same techniques made on the stability conditions, we obtain delay-dependent sufficient conditions for the robust stability of uncertain fuzzy systems with discrete and distributed delays. Moreover, we consider the state feedback stabilization. Based on stability and robust stability conditions, we obtain conditions for the state feedback controller to stabilize the fuzzy time-delay systems. Finally, we give two examples to illustrate our results. Delay-dependent stability conditions obtained here are shown to guarantee a wide stability region.     

Multivariate significance testing and model calibration under uncertainty

The importance of modeling and simulation in the scientific community has drawn interest towards methods for assessing the accuracy and uncertainty associated with such models. This paper addresses the validation and calibration of computer simulations using the thermal challenge problem developed at Sandia National Laboratories for illustration. The objectives of the challenge problem are to use hypothetical experimental data to validate a given model, and then to use the model to make predictions in an untested domain. With regards to assessing the accuracy of the given model (validation), we illustrate the use of Hotelling’s T² statistic for multivariate significance testing, with emphasis on the formulation and interpretation of such an analysis for validation assessment. In order to use the model for prediction, we next employ the Bayesian calibration method introduced by Kennedy and O’Hagan. Towards this end, we discuss how inherent variability can be reconciled with “lack-of-knowledge” and other uncertainties, and we illustrate a procedure that allows probability distribution characterization uncertainty to be included in the overall uncertainty analysis of the Bayesian calibration process.     

Measurement of the thermal conductivity anisotropy in polyimidefilms

Polymer films are playing an important role in the development of micromachined sensors and actuators, fast logic circuits, and organic optoelectronic devices. The thermal properties of polyimide films govern the temporal response of many micromachined thermomechanical actuators, such as ciliary arrays. This work develops three experimental techniques for measuring both the in-plane and the out-of-plane thermal conductivities of spin-coated polyimide films with thicknesses between 0.5 and 2.5 μm, which are common in MEMS. Two of the techniques use transient electrical heating and thermometry in micromachined structures to isolate the in-plane and out-of-plane components. These techniques establish confidence in a third, simpler technique, which measures both components independently and uses IC-compatible processing. The data illustrate the anisotropy in the thermal conductivity of the polyimide films investigated here, with the in-plane conductivity larger by a factor between four and eight depending on film thickness and temperature. The anisotropy diminishes the time constants of thermal actuators made from polyimide films     

A simple self-adaptive Differential Evolution algorithm with application on the ALSTOM gasifier

Differential Evolution (DE) has gathered a reputation for being a powerful yet simple global optimiser with continually outperforming many of the already existing stochastic and direct search global optimisation techniques. It is however well established that DE is particularly sensitive to its control parameters, most notably the mutation weighting factor F. This sensitivity is further studied here and a simple randomised self-adaptive scheme is proposed for the DE mutation weighting factor F. The performance of this algorithm is studied with the use of several benchmark problems and applied to a difficult control systems design case study.     

Voting techniques for expert search

In an expert search task, the users’ need is to identify people who have relevant expertise to a topic of interest. An expert search system predicts and ranks the expertise of a set of candidate persons with respect to the users’ query. In this paper, we propose a novel approach for predicting and ranking candidate expertise with respect to a query, called the Voting Model for Expert Search. In the Voting Model, we see the problem of ranking experts as a voting problem. We model the voting problem using 12 various voting techniques, which are inspired from the data fusion field. We investigate the effectiveness of the Voting Model and the associated voting techniques across a range of document weighting models, in the context of the TREC 2005 and TREC 2006 Enterprise tracks. The evaluation results show that the voting paradigm is very effective, without using any query or collection-specific heuristics. Moreover, we show that improving the quality of the underlying document representation can significantly improve the retrieval performance of the voting techniques on an expert search task. In particular, we demonstrate that applying field-based weighting models improves the ranking of candidates. Finally, we demonstrate that the relative performance of the voting techniques for the proposed approach is stable on a given task regardless of the used weighting models, suggesting that some of the proposed voting techniques will always perform better than other voting techniques. Extended version of ‘Voting for candidates: adapting data fusion techniques for an expert search task’. C. Macdonald and I. Ounis. In Proceedings of ACM CIKM 2006, Arlington, VA. 2006. doi: 10.1145/1183614.1183671.     

Model-based testing of global properties on large-scale distributed systems

ContextLarge-scale distributed systems are becoming commonplace with the large popularity of peer-to-peer and cloud computing. The increasing importance of these systems contrasts with the lack of integrated solutions to build trustworthy software. A key concern of any large-scale distributed system is the validation of global properties, which cannot be evaluated on a single node. Thus, it is necessary to gather data from distributed nodes and to aggregate these data into a global view. This turns out to be very challenging because of the system’s dynamism that imposes very frequent changes in local values that affect global properties. This implies that the global view has to be frequently updated to ensure an accurate validation of global properties.ObjectiveIn this paper, we present a model-based approach to define a dynamic oracle for checking global properties. Our objective is to abstract relevant aspects of such systems into models. These models are updated at runtime, by monitoring the corresponding distributed system.MethodWe conduce real-scale experimental validation to evaluate the ability of our approach to check global properties. In this validation, we apply our approach to test two open-source implementations of distributed hash tables. The experiments are deployed on two clusters of 32 nodes.ResultsThe experiments reveal an important defect on one implementation and show clear performance differences between the two implementations. The defect would not be detected without a global view of the system.ConclusionTesting global properties on distributed software consists of gathering data from different nodes and building a global view of the system, where properties are validated. This process requires a distributed test architecture and tools for representing and validating global properties. Model-based techniques are an expressive mean for building oracles that validate global properties on distributed systems.     

Data and reports: Contents design and users allocation

When a common information system should supply data in a batch processing mode to many various users, the systems analyst is faced with the problem of allocating data among various reports and reports among different users. The traditional approach to that problem is very unstructured. Such a problem is formulated here in a nonlinear binary programming model. Four alternative techniques of solving the model are presented, followed by an example. The main contribution is a systematic and analytic approach toward a major problem in information systems design.     

Supply management for cost minimization in assembly systems with random component yield times

This paper is concerned with inventory control in assembly systems for minimizing production costs. The system manufactures multiple products assembled from various components, and it operates according to a cyclic schedule. At the start of each cycle time, two decisions are made: the product volumes to be assembled during the current cycle, and the component-stock levels to be ordered. For a given decision, there is an associated cost incurred by backlogging of the finished products on one hand, and the component inventory holding cost, on the other hand. The objective here is to balance the two costs so as to minimize their sum. One complicating factor stems from uncertainties in both product demand levels and components yield times. These uncertainties can be modelled by probabilistic means, and hence the cost minimization problem becomes a stochastic problem. This problem can be quite difficult due to the nonlinearity of the equations involved, the mix of integer and continuous parameters, and their large number in moderate-size problems. Our approach in this paper is to first define certain control parameters and thus reduce the number of the variables involved in the optimization problem, and then solve the latter problem by using sophisticated optimization techniques in conjunction with heuristic modelling. We will demonstrate, by numerical means, the resolution of fairly difficult problems and thus establish the viability of the proposed numerical techniques.     

Ellipsoidal techniques for dynamic systems: The problem of control synthesis

This article gives an early account of the application of ellipsoidal techniques to various problems in modeling dynamical systems. The problem of control synthesis for a linear system under bounded controls was selected as the first simple application of these techniques. In forthcoming papers, these results will be extended to the case where unknown but bounded disturbances are present. Guaranteed state estimation—also to be interpreted as a tracking problem—again under unknown but bounded disturbances will also be discussed.Although the problem is treated here for linear systems only, the synthesized system is driven by a nonlinear control strategy and is therefore generically nonlinear. Taking a scheme based on the notion of extremal aiming strategies of N. N. Krasovski, the present article concentrates on constructive solutions generated through ellipsoidal-valued calculus and related approximation techniques for set-valued maps. The primary problem, which originally required an application of set-valued analysis, is substituted for here by one based on ellipsoidal-valued functions. This yields constructive schemes applicable to algorithmic procedures and simulation with computer graphics. Editor: G. Leitmann     

Time-domain validation for sample-data uncertainty models

We consider time-domain model validation for sampled-data systems with H_∞-compatible uncertainty models. These uncertainty models consist of a nominal continuous-time plant model together with given bounds for system uncertainty and signal disturbances. The validation problem is to determine whether or not a given discrete sampled input-output data record is consistent with a postulated uncertainty model. Based on continuous-time interpolation theorems, we provide validation algorithms for unstructured, additive uncertainty models that reduce to convex programming. We treat both linear time-invariant and linear time-varying modeling uncertainty sets     

A brief history and future aspects in automatic cleaning systems for solar photovoltaic panels

The energy or efficiency produced by solar photovoltaic modules is related with the Sun’s available irradiance and spectral content, as well as other factors like environmental, climatic, component performance and inherent system. These dust, dirt and bird droppings are the major reasons for the solar photovoltaic system underperformance. This paper discusses a comprehensive overview of dust problem and the recent developments made on automated cleaning system for solar photovoltaic modules which give brief overview on techniques like electrical, mechanical, chemical and electrostatic. The main objective of the study is to review the literature on solar photovoltaic module automated cleaning techniques for identifying research gaps in the automated cleaning systems.     

KNOWLEDGE-BASED VALIDATION FOR HYDROLOGICAL INFORMATION SYSTEMS

The introduction of systems for automatic data acquisition to monitor and control hydrological basins is a qualitative change in the field of hydrology. The large amount of information available increases the number of processes that can be analyzed with a quantitative approach. In the past, hydrological data validation was done manually by applying the knowledge of experts in the field. This article proposes to solve this problem using AI techniques. As a result, a generic model is defined for the cognitive task of data validation. The model is then applied to a real case.