Desenvolupament d'un sistema expert com a eina per a una millor gestió de la qualitat de les aigües fluvials

Avui en dia no es pot negar el fet que els humans són un component més de les conques fluvials i que la seva activitat afecta enormement la qualitat de les aigües. A nivell europeu, l'elevada densitat de població situada en les conques fluvials ha comportat un increment de la mala qualitat de les seves aigües fluvials. En les darreres dècades l'increment de les càrregues de nutrients en els sistemes aquàtics ha esdevingut un problema prioritari a solucionar per les administracions competents en matèria d'aigua. La gestió dels ecosistemes fluvials no és una tasca fàcil. Els gestors es troben amb què són sistemes molt complexos, donada l'estreta relació existent entre els ecosistemes fluvials i els ecosistemes terrestres que drenen. Addicionalment a la complexitat d'aquests sistemes es troba la dificultat associada de la gestió o control de les entrades de substàncies contaminants tant de fonts puntuals com difoses. Per totes aquestes raons la gestió de la qualitat de les aigües fluvials esdevé una tasca complexa que requereix un enfocament multidisciplinar. Per tal d'assolir aquest enfocament diverses eines han estat utilitzades, des de models matemàtics fins a sistemes experts i sistemes de suport a la decisió. Però, la major part dels esforços han estat encarats cap a la resolució de problemes de reduïda complexitat, fent que molts dels problemes ambientals complexos, com ara la gestió dels ecosistemes fluvials, no hagin estat vertaderament tractats. Per tant, es requereix l'aplicació d'eines que siguin de gran ajuda en els processos de presa de decisions i que incorporin un ampli coneixement heurístic i empíric: sistemes experts i sistemes de suport a la decisió. L'òptima gestió de la qualitat de l'aigua fluvial requereix una aproximació integrada i multidisciplinar, que pot ésser aconseguida amb una eina intel·ligent construïda sobre els conceptes i mètodes del raonament humà. La present tesi descriu la metodologia desenvolupada i aplicada per a la creació i construcció d'un Sistema Expert, així com el procés de desenvolupament d'aquest Sistema Expert, com el principal mòdul de raonament d'un Sistema de Suport a la Decisió Ambiental. L'objectiu principal de la present tesi ha estat el desenvolupament d'una eina d'ajuda en el procés de presa de decisions dels gestors de l'aigua en la gestió de trams fluvials alterats antròpicament per tal de millorar la qualitat de la seva aigua fluvial. Alhora, es mostra el funcionament de l'eina desenvolupada a través de dos casos d'estudi. Els resultats derivats del Sistema Expert desenvolupat, implementat i presentat en la present tesi mostren que aquests sistemes poden ésser eines útils per a millorar la gestió dels ecosistemes fluvials.

Involving users in expert system development

The ultimate criterion of success for interactive expert systems is that they will be used, and used to effect, by individuals other than the system developers. A key ingredient of success in most systems is involving users in the specification and development of systems as they are being built. However, until recently, system designers have paid little attention to ascertaining user needs and to developing systems with corresponding functionality and appropriate interfaces to match those requirements. Although the situation is beginning to change, many developers do not know how to go about involving users, or else tackle the problem in an inadequate way. This paper discusses the need for user involvement and considers why many developers are still not involving users in an optimal way. It looks at the different ways in which users can be involved in the development process and describes how to select appropriate techniques and methods for studying users. Finally, it discusses some of the problems inherent in involving users in expert system development, and recommends an approach which incorporates both ethnographic analysis and formal user testing.

Unified framework for construction of rule based classification systems

Automatic generation of classification rules has been an increasingly popular technique in commercial applications such as Big Data analytics, rule based expert systems and decision making systems. However, a principal problem that arises with most methods for generation of classification rules is the overfit-ting of training data. When Big Data is dealt with, this may result in the generation of a large number of complex rules. This may not only increase computational cost but also lower the accuracy in predicting further unseen instances. This has led to the necessity of developing pruning methods for the simplification of rules. In addition, classification rules are used further to make predictions after the completion of their generation. As efficiency is concerned, it is expected to find the first rule that fires as soon as possible by searching through a rule set. Thus a suit-able structure is required to represent the rule set effectively. In this chapter, the authors introduce a unified framework for construction of rule based classification systems consisting of three operations on Big Data: rule generation, rule simplification and rule representation. The authors also review some existing methods and techniques used for each of the three operations and highlight their limitations. They introduce some novel methods and techniques developed by them recently. These methods and techniques are also discussed in comparison to existing ones with respect to efficient processing of Big Data.

Categorization and construction of rule based systems

Expert systems have been increasingly popular for commercial importance. A rule based system is a special type of an expert system, which consists of a set of ‘if-then‘ rules and can be applied as a decision support system in many areas such as healthcare, transportation and security. Rule based systems can be constructed based on both expert knowledge and data. This paper aims to introduce the theory of rule based systems especially on categorization and construction of such systems from a conceptual point of view. This paper also introduces rule based systems for classification tasks in detail.

Expert system to calculate the cofficient of friction : an approach to enhance traffic safety

Friction plays a key role in causing slipperiness as a low coefficient of friction on the road may result in slippery and hazardous conditions. Analyzing the strong relation between friction and accident risk on winter roads is a difficult task. Many weather forecasting organizations use a variety of standard and bespoke methods to predict the coefficient of friction on roads. This article proposes an approach to predict the extent of slipperiness by building and testing an expert system. It estimates the coefficient of friction on winter roads in the province of Dalarna, Sweden using the prevailing weather conditions as a basis. Weather data from the road weather information system, Sweden (RWIS) was used. The focus of the project was to use the expert system as a part of a major project in VITSA, within the domain of intelligent transport systems

Agent-Based Hybrid Intelligent Systems: An Agent-Based Framework for Complex Problem Solving

Pt. I. Fundamentals of hybrid intelligent systems and agents -- 1. Introduction -- 2. Basics of hybrid intelligent systems -- 3. Basics of agents and multi-agent systems -- Pt. II. Methodology and framework -- 4. Agent-oriented methodologies -- 5. Agent-based framework for hybrid intelligent systems --6. Matchmaking in middle agents -- Pt. III. Application systems -- 7. Agent-based hybrid intelligent system for financial investment
planning -- 8. Agent-based hybrid intelligent system for data mining -- Pt. IV. Concluding remarks -- 9. The less the more -- App. Sample source codes of the agent-based financial planning system

Appropriate choice of aggregation operators in fuzzy decision support systems

Fuzzy logic provides a mathematical formalism for a unified treatment of vagueness and imprecision that are ever present in decision support and expert systems in many areas. The choice of aggregation operators is crucial to the behavior of the system that is intended to mimic human decision making. This paper discusses how aggregation operators can be selected and adjusted to fit empirical data—a series of test cases. Both parametric and nonparametric regression are considered and compared. A practical application of the proposed methods to electronic implementation of clinical guidelines is presented

Investigating information systems analysts' possession of tacit organisational knowledge

Outsourcing of Information Technology (IT) services which are central to business strategy may be risky. Managers have made the outsourcing decision both to concentrate financially on the core competencies and to rid themselves of a troublesome and cost inefficient department. More recent research has, however, cast doubt on the promises of huge savings. In this paper, we consider the likelihood that outsourcing may lead to the loss of organisational knowledge - that organisations outsourcing their total Information Systems operations may also have lost irreplaceable tacit, cross-functional knowledge which subsisted within the minds of the professional systems analysts. The findings of our research revealed that expert systems analysts possess a unique organisational understanding and draw on this knowledge to operate efficiently in their environment. We present a model that will allow future researchers to build on our findings and examine whether outsourcing can lead to a loss of organisational memory.

Building agent-based hybrid intelligent systems

Many complex problems including financial investment planning, foreign exchange trading, knowledge discovery from large/multiple databases require hybrid intelligent systems that integrate many intelligent techniques including expert systems, fuzzy logic, neural networks, and genetic algorithms. However, hybrid intelligent systems are difficult to develop because they have a large number of parts or components that have many interactions. On the other hand, agents offer a new and often more appropriate route to the development of complex systems, especially in open and dynamic environments. In this paper, it is argued that agent technology is well snited for constructing hybrid intelligent systems (especially loosely coupled hybrid intelligent systems) through a successful case study. A great number of heterogeneous computing techniques/packages are easily integlated into the experimental system under a unifying agent framework, which implies that agent technology can greatly facilitate the construction of hybrid intelligent systems.

Co-ordination in multi-agent systems

A multi-agent system is a complex software system which is composed of many relative autonomous smaller softwares called agents. The research on multi-agent systems is concerned with the interaction and coordination among these agents to let them help each other to solve complicated problems, such as finance investment management. The principal contributions represented by these 50 selected papers are "cooperation under uncertainty in distributed expert systems (DESs)", "a tool and algorithms to build DESs", and "information gathering and decision making in multi-agent systems (MASs)".

Expert system for hot forging design

Planning hot forging processes is a time-consuming activity with high costs involved because of the trial-and-error iterative methods used to design dies and to choose equipment and process conditions. Some processes demand many months to produce forged parts with controlled shapes, dimensions and microstructure. This paper shows how expert systems can help engineers to reduce the time needed to design precision forged parts and dies from machined parts. The software ADHFD interfacing MS Visual Basic v.5.0 and SolidEdge v.3.0 was used to design flashless hot forged gears, chosen from families of gears. © 1998 Elsevier Science S.A. All rights reserved.

Novel expert system for defining power quality compensators

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Expert system to detect incipient fauts in rotating machines using fuzzy logic and virtual instrumentation

This paper refers to the design of an expert system that captures a waveform through the use of an accelerometer, processes the signal and converts it to the frequency domain using a Fast Fourier Transformer to then, using artificial intelligence techniques, specifically Fuzzy Reasoning, it determines if there is any failure present in the underlying mode of the equipment, such as imbalance, misalignment or bearing defects.