A Knowledge-Based Approach for Microwire Casting Plant Control

This chapter presents the main ideas and preliminary results of an applied research project concerning the development of an intelligent plant for microwire casting. The properties of glass-coated microwires are useful for a variety of sensor applications. On the other hand, the process of casting can be one of the methods of nanotechnology and advanced materials. In microwire continuous casting, the main control problem is to maintain the optimum thermal and flow conditions of the process, in order to fabricate the microwire of a given stable diameter. Unlike a conventional casting plant, we propose to use a video camera to take the picture of the molten drop and to control the casting process by means of a knowledge based system. For this reason, a model, that is capable of taking into account the current features of the process and of describing the shape of the drop at each time, is developed. The model presented here should allow us to estimate the geometry of the metal-filled capillary and predict the diameter of microwire at each time during the casting process.

Knowledge Redundancy, Environmental Shocks, and Agents’ Opportunism

Notwithstanding the warning of myopic view, when giving too much emphasis to the short run and stable environments, efficiency is usually claimed by standard economics as the main goal of competitive firms. This is challenged by management and organization scholars, who argue that, in presence of strong uncertainty due to environmental turbulence, slack resources can be a competitive advantage. In order to put some sound block in this debate through, this paper tests four groups of hypotheses on an agent-based model of industry competitiveness based on suppliers’ quality. It innovates current literature in two ways: first, it considers redundancy in terms of organizational knowledge, and not in terms of personnel or financial assets or other types of resources, which are usually taken as object of study. Secondly, it compares the effects of two forms of perturbations: environmental shock and opportunism. The results show that these two forms impact differently on industry profitability and that knowledge redundancy can (limitedly) compensate the effects of environmental shocks but not of opportunism. Moreover, it demonstrates that, as agents exchange (and accumulate) more information, knowledge efficiency declines, but less than proportionally to the increase of knowledge exchange.

Expert Guided Autonomous Mobile Robot Learning

In the control of autonomous mobile robots there exist two types of control: global control and local control. The requirement to solve global and local tasks arises respectively. This chapter concentrates on local tasks and shows that robots can learn to cope with some local tasks within minutes. The main idea of the chapter is to show that, while creating intelligent control systems for autonomous mobile robots, the beginning is most important as we have to transfer as much as possible human knowledge and human expert-operator skills into the intelligent control system. Successful transfer ensures fast and good results. One of the most advanced techniques in robotics is an autonomous mobile robot on-line learning from the experts’ demonstrations. Further, the latter technique is briefly described in this chapter. As an example of local task the wall following is taken. The main goal of our experiment is to teach the autonomous mobile robot within 10 minutes to follow the wall of the maze as fast and as precisely as it is possible. This task also can be transformed to the obstacle circuit on the left or on the right. The main part of the suggested control system is a small Feed-Forward Artificial Neural Network. In some particular cases – critical situations – “If-Then” rules undertake the control, but our goal is to minimize possibility that these rules would start controlling the robot. The aim of the experiment is to implement the proposed technique on the real robot. This technique enables to reach desirable capabilities in control much faster than they would be reached using Evolutionary or Genetic Algorithms, or trying to create the control systems by hand using “If-Then” rules or Fuzzy Logic. In order to evaluate the quality of the intelligent control system to control an autonomous mobile robot we calculate objective function values and the percentage of the robot work loops when “If-Then” rules control the robot.

Active Learning in Discrete-Time Stochastic Systems

A general approach to self-learning based on the ideas of adaptive (dual) control is presented. This means that we consider the control problem for a stochastic system with uncertainty as a leading example. Some system’s parameters are unknown and modeled as random variables with known a’priori distribution function. To optimize an objective function, a controller has to learn the system’s parameter values. The main difficulty comes from the fact that he has to optimize the objective function parallely, i.e., at the same time. Moreover, these two goals considered separately not necessarily coincide and the main problem in the adaptive control is to find the trade-off between them. Looking from the self-learning perspective the two directions are visible. The first is to extract the learning procedure from an optimal adaptive control law and to formulate it as a Cybernetic Principle of self-learning. The second is to consider a control problem with the special objective function. This function has to measure our knowledge about unknown parameters. It can be the Fisher information (Banek & Kulikowski, 2003), the joint entropy (for example Saridis, 1988; Banek & Kozlowski, 2006), or something else. This objective function in the control problem will force a controller to steer a system along trajectories that are rich in information about unknown quantities. In this chapter the authors follow the both directions. First they obtain conditions of optimality for a general adaptive control problem and resulting algorithm for computing extremal controls. The results are then applied to the simple example of the Linear Quadratic Gaussian (LQG) problem. By using analytical results and numerical simulations the authors are able to show how control actions depend on the a’piori knowledge about a system. The first conclusion is that a natural, methodological candidate for the optimal self-learning strategy, the “certainty equivalence principle”, fails to satisfy optimality conditions. Optimal control obtained in the case of perfect system’s knowledge is not directly usable in the partial information case. The need of active learning is an essential factor. The differences between controls mentioned above are visible on a level of computations and should be interpreted on a higher level of cybernetic thinking in order to give a satisfactory explanation, perhaps in the form of another principle. Under absence of the perfect knowledge of parameters values, the control actions are restricted by some measurability requirement and the authors compute the Lagrange multiplier associated with this “information constraint”. The multiplier is called a “dual” or “shadow” price and in the literature of the subject is interpreted as an incremental value of information. The authors compute the Lagrange multiptier and analyze its evolution to see how its value changes as the time goes on. As a second sort of conclusion the authors get the self-learning characteristic coming from the information theory point of view. In the last section the authors follow the second direction. In order to estimate the speed of self-learning they choose as an objective function, the conditional entropy. They state the optimal control problem for minimizing the conditional entropy of the system under consideration. Using general results obtained at the beginning, they get the conditions of optimality and the resulting algorithm for computing the extremal controls. Optimal evolution of the conditional entropy tells much about intensivity of self-learning and its time distribution.

Towards Knowledge Driven Individual Integrated Indicators of Innovativeness

Innovativeness of the enterprises is a key factor for the development of a national economy and has a crucial impact on the prosperity of a country. Governments spend a lot of efforts developing, organizing and then implementing national innovation systems. Proper functioning of such a system requires a lot of information to be gathered. The situation has to be constantly monitored as the innovation is an inherently dynamic phenomenon. An important goal of information gathering is learning a profile and specifics of the most innovative enterprises, promote them and make them more visible. Due to that some non-standard data analysis techniques are needed which can provide results of data gathering in a form suitable for the specific goals of the analysis. In this paper a pioneering system for innovativeness evaluation based on integrated indicators constructed for individual enterprises in Poland is described. An evaluation methodology has been developed, incorporating both quantitative and qualitative characteristics of the enterprises. The linguistic summaries of data are shown to be a promising data analysis tool, meeting the criteria which are discussed as relevant for the task considered. Briefly, these summaries make it possible to grasp the very essence of the collected data and communicate it in an intuitive, natural language like form.

Self-Tuning Control Systems

A concise technical overview of some of the key ‘landmark’ developments in self-tuning control (STC) are presented. The notion of two coupled sub-algorithms forming the basis of STC together with enhancements to produce adaptive on-line procedures is discussed as well as the potential limitations of such schemes. The techniques covered include optimal minimum variance, sub-optimal pole-placement and long range model-based predictive control. Based on the experiences of the authors in the industrial application of STC, extensions of the standard linear model-based approaches to encompass a class of bilinear model-based schemes, are proposed. Some on-going developments and future research directions in STC for bilinear systems are highlighted. These include the requirements for combined algorithms for control and fault diagnosis and the need for models of differing complexities.

Perspectives of Multivariable Fuzzy Control

In this work, the situation and trends in the application of fuzzy logic control to multivariable systems are analyzed. The basic steps in designing a control system are considered. The discussion is carried out first on heuristic and reasoning approaches and, later, on function-approximation fuzzy paradigms. In both cases, apart from general considerations, some specific issues arising when considering multivariable setups are considered.

Knowledge Exchange in Collaborative Networks of Enterprises

The problem of information exchange in the inter-enterprise cooperation process design is presented in the chapter. Development of an innovative character of an enterprise requires facilitating the initiation, creation, and extension of cooperative links among enterprises. Collaborative design process gathers enterprises which have to achieve a common objective related to a new product – innovation by information and knowledge sharing, with a high level of activities’ coordination. Development of methods and ways of data exchange in cooperation enables the creation of computer systems aiding production cooperation. System analysis of the cooperation process of enterprises, which is one of the most dynamically developing field of computer systems application in economic activity, and use of knowledge as the base of description and management described in this chapter are shown as the right approach to overcome different area specifics. Formal description of the cooperation process could be utilized also for innovation processes and links. Chapter could open new fields of application and research production engineering knowledge’s application in computer systems.

Application of Two-Stage Adaptive Decision Making System Based on Takagi-Sugeno Model for Scenario Selection in Rehabilitation Process

This chapter focuses on selected problems of complex systems identification. The first part of the chapter is devoted to identification problems in general. The tasks of determination of the plant parameters and choice of the best model are given. Then, authors describe problems of complex systems, i.e.: identification with use of limited measurements, global identification and two-stage identification. The last one is presented in details. In order to illustrate proposed methods, an adaptive system with two-stage identification and its application to biomedical problem is presented.

On Analogue TMR System

This chapter presents a reliability model of the TMR (Triple Modular Redundancy) system based on analogue measurement channels. While reliability modelling of the standard TMR system (based on digital channels) has been well described in many previous publications, an applicable reliability solution for analogue measurement channels is still missing. First, the structure of analogue measurement channel is described in this chapter. Then, the reliability model of the wiring system is introduced. Next, the standard TMR model is presented and its reliability model is mentioned. An analogue TMR measurement channel system is introduced and its reliability model based on Markov processes is presented. Then the reliability model of the communication channel is described. Finally, the reliability of this model is analytically calculated and the solution is applied to an example.