Expert system to control and to design closed loop conveyor systems

A conveyor system is an important part of a manufacturing system. As such, the conveyor system must comply with all the requirements of a modern manufacturing system: high flexibility, high efficiency, and high speed—smart reasoning processes to generate future positions based on a given current status. Because a huge number of figures and numerical manipulatuions are associated with the conveyor systems operations, the traditional numerical control techniques cannot satisfy the requirements to initiate and to control operations. New techniques based on very efficient reasoning processes are required. This article discusses an expert system that consists of a knowledge base and an inference engine that was developed to control a converyor system real-time operation. The computer runs that were performed during this research lead to the conclusion that the developed expert system can be employed to control the conveyor system real time operations very effectively. The developed expert system is considered as a reliable simulator of a conveyor system which can be implemented to explore parameters interrelationships at the phase of system design. Computer runs were performed to analyze the interrelationships between operational parameters which characterize the explored conveyor system. The expert system was programmed in a way that provides a generic simulator, which can be employed in a large variety of conveyor systems.     

Successful requirements management

Conclusion By way of a conclusion, the preceding discussion has identified a relatively complex web of interrelationships that need to be managed. Too often the technical details are focused on to the detriment of the sponsoring business. However, if requirements are to be managed successfully, there is a need for adequate tool support. In my opinion, the requirements management tools which are currently available do not provide the sophisticated level of support needed to adequately manage the complexity of linking requirements, acceptance criteria, constraints, benefits and solutions.     

UM-RTCOM: An analyzable component model for real-time distributed systems

Component-based development is a key technology in the development of software for modern real-time systems. However, standard component models and tools are not suitable for this type of system, since they do not explicitly address real time, memory or cost constraints. This paper presents a new predictable component model for real-time systems (UM-RTCOM) together with a set of tools to support it. The environment allows new components to be developed which can then be assembled to build complete applications, including hardware interaction. The model includes support for real-time analysis at the component and application level. The analysis is achieved by combining component meta-information in the form of an abstract behaviour model and a method to measure worst-case execution times in the final platform. Additionally, we propose an implementation model based on RT-CORBA where the developer uses the UM-RTCOM components and a set of tools to map these elements to elements of the desired platform. In order to apply our proposals, we have used the model and tools in real applications specifically in the context of nuclear power plant simulators.     

Optimal number of hosts in a distributed system based on cost criteria

Redundant or distributed systems are increasingly used in system design so that the required reliability and availability can be easily achieved. However, such an approach requires additional resources that can be very costly. Hence, how to design and test such a system in the most cost-effective way is of concern to the developers. A general cost model and a solution algorithm are presented for the determination of the optimal number of hosts and optimal system debugging time that minimize the total cost while achieving a certain performance objective. During testing, software faults are corrected and the reliability shows an increasing trend, and hence system reliability increases. A general system model is constructed based on a Markov process with software reliability and availability obtained from software reliability growth models. The optimization problem is formulated based on the cost criteria and the solution procedure is described. An application example is presented.     

Rationality in Information Systems Support to Decision Making

Management information systems serve business organizations by providing information for decision making. Various types of systems serve different types of decision contexts. The philosophic basis of information system support is discussed. The rational (or normative) philosophy is widely used, and appears in business theory in the form of agency theory and transaction cost analysis. While this approach has been valuable in some contexts, there are other contexts where the rational approach has limited in utility for real business decision making. Decision makers need to consider subjective factors to enable them to cope with the high levels of uncertainty, incomplete understanding, and imperfect data typical of dynamic open systems. There are alternative philosophies upon which to base decision-making that are appropriate for specific decision contexts. Churchman identified empirical, multi-perspective frameworks, dialectic, and cause-and-effect inquiring systems as alternatives to the rational (normative) system. A number of information system tools, such as decision support systems, expert systems, and group support systems can be supported by models based on philosophies other than rational models. A more empirically based philosophy, with decision-makers balancing hypothesis generation and observations of performance, is often more appropriate. The relationship between Churchman's inquiring systems and information system types are discussed.     

A framework for the application of metaheuristics to?tasks-to-processors assignation problems

The optimization of the execution time of a parallel algorithm can be achieved through the use of an analytical cost model function representing the running time. Typically the cost function includes a set of parameters that model the behavior of the system and the algorithm. In order to reach an optimal execution, some of these parameters must be fitted according to the input problem and to the target architecture. An optimization problem can be stated where the modeled execution time for the algorithm is used to estimate the parameters. Due to the large number of variable parameters in the model, analytical minimization techniques are discarded. Exhaustive search techniques can be used to solve the optimization problem, but when the number of parameters or the size of the computational system increases, the method is impracticable due to time restrictions. The use of approximation methods to guide the search is also an alternative. However, the dependence on the algorithm modeled and the bad quality of the solutions as a result of the presence of many local optima values in the objective functions are also drawbacks to these techniques. The problem becomes particularly difficult in complex systems hosting a large number of heterogeneous processors solving non-trivial scientific applications. The use of metaheuristics allows for the development of valid approaches to solve general problems with a large number of parameters. A well-known advantage of metaheuristic methods is the ability to obtain high-quality solutions at low running times while maintaining generality. We propose combining the parameterized analytical cost model function and metaheuristic minimization methods, which contributes to a novel real alternative to minimize the parallel execution time in complex systems. The success of the proposed approach is shown with two different algorithmic schemes on parallel heterogeneous systems. Furthermore, the development of a general framework allows us to easily develop and experiment with different metaheuristics to adjust them to particular problems.     

MND-SCEMP: an empirical study of a software cost estimation modeling process in the defense domain

The primary focus of weapon systems research and development has moved from a hardware base to a software base and the cost of software development is increasing gradually. An accurate estimation of the cost of software development is now a very important task in the defense domain. However, existing models and tools for software cost estimation are not suitable for the defense domain due to problems of accuracy. Thus, it is necessary to develop cost estimation models that are appropriate to specific domains. Furthermore, most studies of methodology development are aligned with generic methodologies that do not consider the pertinent factors to specific domains, whereas new methodologies should reflect specific domains. In this study, we apply two generic methodologies to the development of a software cost estimation model, before suggesting an integrated modeling process specifically for the national defense domain. To validate our proposed modeling process, we performed an empirical study of 113 software development projects on weapon systems in Korea. A software cost estimation model was developed by applying the proposed modeling process. The MMRE value of this model was 0.566 while the accuracy was appropriate for use. We conclude that the modeling process and software cost estimation model developed in this study is suitable for estimating resource requirements during weapon system development in South Korea’s national defense domain. This modeling process and model may facilitate more accurate resource estimation by project planners, which will lead to more successful project execution.     

Decision support for integrated cash management

Cash management has attracted the increasing attention of both academicians and practitioners in recent time. The expanding role and responsibilities of cash managers and corporate treasurers is likely to increase the focus on cash management as a vital organizational function. Academic research, however, has primarily focused on providing analytical tools to solve well structured problems that are relatively isolated in nature. Conspicuously, no attempt has been made to integrate the various sub-problems of cash management explicitly recognizing interrelationships among the sub-problems as well as between the sub-problems and other financial decisions. More importantly, there is an even greater need to provide a framework that in addition to recognizing the above interrelationships will enable the cash manager to recognize a more inclusive set of dependencies that exist in practice.Decision support systems have the potential to overcome the above deficiencies to a significant extent. This paper is aimed at providing a conceptual framework for designing an effective model-based decision support system (DSS) for integrated cash management. The framework should form a useful basis for any attempts at designing computer-based support systems for cash management.     

Exploiting Object Escape and Locking Information in Partial-Order Reductions for Concurrent Object-Oriented Programs

Explicit-state model checking tools often incorporate partial-order reductions to reduce the number of system states explored (and thus the time and memory required) for verification. As model checking techniques are scaled up to software systems, it is important to develop and assess partial-order reduction strategies that are effective for addressing the complex structures found in software and for reducing the tremendous cost of model checking software systems. In this paper, we consider a number of reduction strategies for model checking concurrent object-oriented software. We investigate a range of techniques that have been proposed in the literature, improve on those in several ways, and develop five novel reduction techniques that advance the state of the art in partial-order reduction for concurrent object-oriented systems. These reduction strategies are based on (a) detecting heap objects that are thread-local (i.e., can be accessed by a single thread) and (b) exploiting information about patterns of lock-acquisition and release in a program (building on previous work). We present empirical results that demonstrate upwards of a hundred fold reduction in both space and time over existing approaches to model checking concurrent Java programs. In addition to validating their effectiveness, we prove that the reductions preserve LTL_?X properties and describe an implementation architecture that allows them to be easily incorporated into existing explicit-state software model checkers.     

APPLICATIONS DEVELOPMENT TOOLS

A serious problem that often occurs during systems development is the inappropriate use of applications development tools. Two primary drawbacks of this misuse are the cost of purchasing unneeded tools and the loss of productivity because of an increase in project completion time. However, the use of appropriate tools (e.g., CASE, DBMS, and project management packages) can result in the efficient and proper development of application systems.     

On hybrid systems and closed-loop MPC systems

The following five classes of hybrid systems were recently proven to be equivalent: linear complementarity, extended linear complementarity, mixed logical dynamical systems, piecewise affine systems and max-min-plus-scaling systems. Some of the equivalences were obtained under additional assumptions, such as boundedness of certain system variables. In this paper, for linear or hybrid plants in closed-loop with a model predictive control (MPC) controller based on a linear model fulfilling linear constraints on input and state variables and utilizing a quadratic cost criterion, we provide a simple and direct proof that the closed-loop system is a subclass of any of the former five classes of hybrid systems. This result is of extreme importance, as it opens up the use of tools developed for the mentioned hybrid model classes, such as (robust) stability and safety analysis tools, to study closed-loop properties of MPC     

Computer support for mechatronic control system design

This paper discusses the demands for proper tools for computer aided control system design of mechatronic systems and identifies a number of tasks in this design process. Real mechatronic design, involving input from specialists from varying disciplines, requires that the system can be represented in multiple views. Several tools are already available but there are still substantial shortcomings. The paper gives indications about the developments needed to come to better design tools in the future. A specific example is worked out in more detail, i.e., automated performance assessment of mechatronic motion systems during the conceptual design stage.     

Availability Modeling and Cost Optimization for the Grid Resource Management System

Grid computing is a recently developed technique for complex systems with large-scale resource sharing, wide-area communication, and multi-institutional collaboration. Although the development tools and techniques for the grid have been extensively investigated, the availability of the grid resource management system (RMS) has not been comprehensively studied. In order to contribute to this lacking but important field, this paper first models the grid RMS availability by considering both the failures of resource management (RM) servers and the length limitation of request queues. A hierarchical Markov reward model is implemented to evaluate the grid RMS availability. Based on the availability model, an optimization problem for designing the grid RMS is studied in order to minimize the cost by determining the best number of RM servers. Then, the sensitivity analysis is conducted, and a dynamic switching scheduling method is further presented based on the sensitivity analysis.     

Dynamics modeling and culling

The tools described, permit including large numbers of complex dynamic models in a VRML world easily and efficiently while maintaining high frame rates. We describe three tools that together provide an environment for authoring cullable, dynamic, rigid-body objects in VRML and Java: a code transformation tool that exploits approximations to dynamical systems to enable culling; a runtime layer generator, which defines a simple standard interface between a VRML browser and dynamical systems described in Java; a rigid-body modeler, which allows users to interactively design the runtime layer and preview the dynamic behavior. The article describes these tools, including some example systems, and discusses the runtime performance improvements obtained. Our tools are applicable if the spatial range of the dynamic model can be bounded by a static volume, the model is closed to outside influence, the underlying equations are continuous, and the dimension (number of degrees of freedom) of the system is small. Note that while the article focuses on VRML and Java as the target environment, the underlying techniques apply to any rendering and language environment     

Estimation and diagnosis using multi-models with application to a wastewater treatment plant

Process diagnosis is still considered a challenging engineering problem. Technological and also environmental systems have complex behaviors often involving nonlinear relationships. When confronted to such systems, there is a need to build systems that can operate over a wide range of operating conditions. For that it is very attractive to appeal to a decomposition of the system model into a number of simpler linear models. This paper mainly focuses on the use of multi-models for process diagnosis. It is shown how the traditional tools of the linear automatic can be wide and applied to multi-model structures. A proportional multi-integral observer is used for fault diagnosis using banks of observers to generate structured residuals. The performances of the proposed diagnosis method are highlighted through the application to a wastewater treatment plant model (WWTP), which is an uncertain nonlinear system affected by unknown inputs.     

Two-echelon supply chain inventory model with controllable lead time and service level constraint

This paper considers a two-echelon supply chain inventory problem consisting of a single-vendor and a single-buyer. In the system under study, a vendor produces a product in a batch production environment and supplies it to a buyer facing a stochastic demand, which is assumed to be normally distributed. Also, buyer’s lead time is controllable which can be shortened at an added cost and all shortages are backordered. A model has been formulated for an integrated vendor–buyer problem to jointly determine the optimal order quantity, lead time and the number of shipments from the vendor to the buyer during a production cycle while minimizing the total expected cost of the vendor–buyer integrated system. It is often difficult to estimate the shortage cost in inventory systems. Therefore, instead of having a shortage cost term in the objective function, a service level constraint (SLC) is included in the model that requires a certain proportion of demands to be met in each cycle. An efficient procedure has been suggested to find the bounds on number of shipments and then, an algorithm is developed to obtain the optimal solution of the proposed model. A numerical example is included to illustrate the algorithmic procedure and the effects of key parameters are studied to analyze the behavior of the model. Finally, the savings of buyer and vendor are investigated from implementation of joint optimization model over the model in which they minimize their own cost independently.     

Complex Probabilistic Modeling with Recursive Relational Bayesian Networks

A number of representation systems have been proposed that extend the purely propositional Bayesian network paradigm with representation tools for some types of first-order probabilistic dependencies. Examples of such systems are dynamic Bayesian networks and systems for knowledge based model construction. We can identify the representation of probabilistic relational models as a common well-defined semantic core of such systems.Recursive relational Bayesian networks (RRBNs) are a framework for the representation of probabilistic relational models. A main design goal for RRBNs is to achieve greatest possible expressiveness with as few elementary syntactic constructs as possible. The advantage of such an approach is that a system based on a small number of elementary constructs will be much more amenable to a thorough mathematical investigation of its semantic and algorithmic properties than a system based on a larger number of high-level constructs. In this paper we show that with RRBNs we have achieved our goal, by showing, first, how to solve within that framework a number of non-trivial representation problems. In the second part of the paper we show how to construct from a RRBN and a specific query, a standard Bayesian network in which the answer to the query can be computed with standard inference algorithms. Here the simplicity of the underlying representation framework greatly facilitates the development of simple algorithms and correctness proofs. As a result we obtain a construction algorithm that even for RRBNs that represent models for complex first-order and statistical dependencies generates standard Bayesian networks of size polynomial in the size of the domain given in a specific application instance.     

<Emphasis Type="Italic">HumanPT</Emphasis>: An Open-source,HumanPT Architecture-based,Robotic Application for Low Cost Robotic Tasks

In this paper, first HumanPT architecture for low cost robotic applications is presented. HumanPT architecture differs than other architectures because it is implemented on existing robotic systems (robot  robotic controller) and exploits the minimum communication facilities for real-time control that these systems provide. It is based on well-known communication methods like serial communication (USB, RS232, IEEE-1394) and windows sockets (server–client model) and permits an important number of different type of components like actuators, sensors and particularly vision systems to be connected in a robotic system. The operating system (OS) used is Microsoft Windows, the most widely spread OS. The proposed architecture exploits features of this OS that is not a real-time one, to ensure – in case that the robotic system provide such a facility – control and real time communication with the robotic system controller and to integrate by means of sensors and actuators an important number of robotic tasks and procedures. As implementation of this architecture, HumanPT robotic application and experimental results concerning its performance and its implementation in real tasks are provided. HumanPT robotic application, developed in Visual C++, is an integrated, but simultaneously an open-source software that can be adapted in different types of robotic systems. An important number of robotic tasks or procedures including sensors and particularly vision systems can be generated and executed. Small enterprises by means of the proposed architecture and the open source software can be automated at low cost enhancing in this way their production.     

A computational technique for the control of systems described by partial difference equations†

A penalty function method is applied to the determination of optimal controls for systems described by partial difference equations. The use of this penalty function method known as the ?-technique allows us to incorporate the dynamical constraints of the distributed parameter system into a new objective function. The conditions for the solution which minimizes the new objective function and the interrelationships between this solution and the solution which minimizes the original objective function are given. These interrelationships also suggest computational schemes for the synthesis of optimal controls for linear and non-linear distributed systems.