A NEW APPROACH TO THE RULE-BASED SYSTEMS DESIGN AND IMPLEMENTATION PROCESS

The paper discusses selected problems encountered in practical rule-based systems (RBS) design and implementation. To solve them XTT, a new visual knowledge representation is introduced. Then a complete, integrated RBS design, implementation and analysis methodology is presented. This methodology is supported by a visual CASE tool called Mirella.The main goal is to move the design procedure to a more abstract, logical level, where knowledge specification is based on use of abstract rule representation. The design specification is automatically translated into Prolog code, so the designer can focus on logical specification of safety and reliability. On the other hand, system formal aspects are automatically verified on-line during the design, so that its verifiable characteristics are preserved

The software-cycle model for re-engineering and reuse

This paper reports on the progress of a study which will contribute to our ability to perform high-level, component-based programming by describing means to obtain useful components, methods for the configuration and integration of those components, and an underlying economic model of the costs and benefits associated with this approach to reuse. One goal of the study is to develop and demonstrate methods to recover reusable components from domain-specific software through a combination of tools, to perform the identification, extraction, and re-engineering of components, and domain experts, to direct the applications of those tools. A second goal of the study is to enable the reuse of those components by identifying techniques for configuring and recombining the re-engineered software. This component-recovery or software-cycle model addresses not only the selection and re-engineering of components, but also their recombination into new programs. Once a model of reuse activities has been developed, the quantification of the costs and benefits of various reuse options will enable the development of an adaptable economic model of reuse, which is the principal goal of the overall study. This paper reports on the conception of the software-cycle model and on several supporting techniques of software recovery, measurement, and reuse which will lead to the development of the desired economic model

Binary Linear Classification and Feature Selection via Generalized Approximate Message Passing

For the problem of binary linear classification and feature selection, we propose algorithmic approaches to classifier design based on the generalized approximate message passing (GAMP) algorithm, recently proposed in the context of compressive sensing. We are particularly motivated by problems where the number of features greatly exceeds the number of training examples, but where only a few features suffice for accurate classification. We show that sum-product GAMP can be used to (approximately) minimize the classification error rate and max-sum GAMP can be used to minimize a wide variety of regularized loss functions. Furthermore, we describe an expectation-maximization (EM)-based scheme to learn the associated model parameters online, as an alternative to cross-validation, and we show that GAMP's state-evolution framework can be used to accurately predict the misclassification rate. Finally, we present a detailed numerical study to confirm the accuracy, speed, and flexibility afforded by our GAMP-based approaches to binary linear classification and feature selection

Visual-based Guidance System for a 6-DOF Robot

The idea of this bachelor’s thesis is to develop a position-based visual servoing system with two webcams that allows an anthropomorphic robot ABB IRB 120 with six degrees of freedom (DOF) to be guided in real time by an operator, by triangulating a specific target moved within the field of view (FOV) of a machine vision system. For this kind of motion control based on visual data input, termed visual servoing control, we need a stereo vision system to acquire the images of the target since three-dimensional information is required to perform object tracking with six DOF. These images are processed by a MATLAB ap-plication running in a remote PC. The current coordinates of the target referred to the left camera reference frame are extracted from the images and sent through an Ethernet connec-tion to the robot controller, which is programmed to receive the vectors and move its tool centre point (TCP) to the demanded position within its workspace.Grado en Ingeniería en Electrónica Industrial y Automátic

Incorporating practice theory in sub-profile models for short term aggregated residential load forecasting

Aspirations of grid independence could be achieved by residential power systems connected only to small highly variable loads, if overall demand on the network can be accurately anticipated. Absence of the diversity found on networks with larger load cohorts or consistent industrial customers, makes such overall load profiles difficult to anticipate on even a short term basis. Here, existing forecasting techniques are employed alongside enhanced classification/clustering models in proposed methods for forecasting demand in a bottom up manner. A Markov Chain based sampling technique derived from Practice Theory of human behavior is proposed as a means of providing a forecast with low computational effort and reduced historical data requirements. The modeling approach proposed does not require seasonal adjustments or environmental data. Forecast and actual demand for a cohort of residential loads over a 5 month period are used to evaluate a number of models as well as demonstrate a significant performance improvement if utilized in an ensemble forecast

Cartesian institutions with evidence: Data and system modelling with diagrammatic constraints and generalized sketches

Data constraints are fundamental for practical data modelling, and a verifiable conformance of a data instance to a safety-critical constraint (satisfaction relation) is a corner-stone of safety assurance. Diagrammatic constraints are important as both a theoretical concepts and a practically convenient device. The paper shows that basic formal constraint management can well be developed within a finitely complete category (hence the reference to Cartesianity in the title). In the data modelling context, objects of such a category can be thought of as graphs, while their morphisms play two roles: of data instances and (when being additionally labelled) of constraints. Specifically, a generalized sketch $S$ consists of a graph $G_S$ and a set of constraints $C_S$ declared over $G_S$, and appears as a pattern for typical data schemas (in databases, XML, and UML class diagrams). Interoperability of data modelling frameworks (and tools based on them) very much depends on the laws regulating the transformation of satisfaction relations between data instances and schemas when the schema graph changes: then constraints are translated co- whereas instances contra-variantly. Investigation of this transformation pattern is the main mathematical subject of the paperComment: 35 pages. The paper will be presented at the conference on Applied Category Theory, ACT'2