Orides Morandin

A genetic programming based system for the automatic construction of image filters

The manual selection of linear and nonlinear operators for producing image filters is not a trivial task in practice, so new proposals that can automatically improve and speed up the process can be of great help. This paper presents a new proposal for constructing image filters using an evolutionary programming approach, which has been implemented as the IFbyGP software. IFbyGP employs a variation of the Genetic Programming algorithm GP and can be applied to binary and gray level image processing. A solution to an image processing problem is represented by IFbyGP as a set of morphological, convolution and logical operators. The method has a wide range of applications, encompassing pattern recognition, emulation filters, edge detection, and image segmentation. The algorithm works with a training set consisting of input images, goal images, and a basic set of instructions supplied by the user, which would be suitable for a given application. By making the choice of operators and operands involved in the process more flexible, IFbyGP searches for the most efficient operator sequence for a given image processing application. Results obtained so far are encouraging and they stress the feasibility of the proposal implemented by IFbyGP. Also, the basic language used by IFbyGP makes its solutions suitable to be directly used for hardware control, in a context of evolutionary hardware. Although the proposal implemented by IFbyGP is general enough for dealing with binary, gray level and color images, only applications using the first two are considered in this paper; as it will become clear in the text, IFbyGP aims at the direct use of induced sequences of operations by hardware devices. Several application examples discussing and comparing IFbyGP results with those obtained by other methods available in the literature are presented and discussed.

Intelligent FPGA based system for shape recognition

Mathematical morphology supplies powerful tools for low level image analysis, with applications in many areas. In this paper, the development of a novel reconfigurable hardware using a genetic algorithm and a pipeline architecture is proposed for the task of shape recognition in binary images. For the recognition process, a large sized convex structuring element representing the object shape to be recognized is decomposed into the architecture stages. Each stage can handle structuring elements of a limited size. In this approach, a genetic algorithm was used to decompose this structuring element. Thus, a simple erosion performed in each stage is used to detect the goal object. The hardware is capable of processing binary images at high speed. The developed system is based on FPGAs. Our approach represents an intelligent mechanism to reconfigure the pipeline architecture, it is different from other systems found in the literature, and the obtained results are promising.

A search method using Genetic Algorithm for production reactive scheduling of manufacturing systems

The problem for scheduling the manufacturing systems production involves the modeling task and the application of a technique to solve it. There are some ways used to model such problem and some search strategies have been applied on the model to find a solution. The solution has to consider performances parameters like makespan or another. However, depending on the size and complexity of the system, the response time becomes critical, mostly when itpsilas necessary a reschedule. Researches aim to use genetic algorithms as a search method to solve the scheduling problem. This work proposes the use of genetic algorithm to solve this problem having as performance criteria the minimum makespan and the response time.

An Adaptive Genetic Algorithm based approach for production reactive scheduling of manufacturing systems

The problem for scheduling the manufacturing systems production involves the system modeling task and the application of a technique to solve it. There are several ways used to model the scheduling problem and search strategies have been applied on the models to find a solution. The solutions consider performance parameters like makespan. However, depending on the size and complexity of the system, the response time becomes critical, mostly when itpsilas necessary to reschedule. Researches aim to use Genetic Algorithms as a search method to solve the scheduling problem. This paper proposes the use of Adaptive Genetic Algorithm (AGA) to solve this problem having as performance criteria the minimum makespan and the response time. The probability of crossover and mutation is dynamically adjusted according to the individualpsilas fitness value. The proposed approach is compared with a traditional Genetic Algorithm (GA).

Virtual Petri nets as a modular modeling method for planning and control tasks of FMS

The modeling of automated manufacturing systems has been studied to cope with the production planning and control problems. Petri Nets have been applied to model these systems because they provide resources to represent the systems behavior. When the systems are too large or complex, the modeling task is difficult and the elements in the model are too many for a simple under-standing and analyze task. This work proposes an method for modeling these systems in order to minimize the difficulties mentioned. The proposed method introduces the virtual Petri net (VPN) and a modular strategy considering the shared resources and the alternative process planning. Using this method, a system has been modeled, initially considering the systems elements for the modeling job, and then a modules linking way can be used in order to build the complete model.

A modular modeling approach for automated manufacturing systems based on shared resources and process planning using Petri nets

The modeling of automated manufacturing systems has been studied to cope with production planning and control problems. Petri nets have been applied to model these systems because they provide resources to represent the system behavior. When the systems are too large or complex, the modeling task is difficult and the elements in the model are too many for a simple understanding and analysis. This paper proposes an approach to modeling these systems to minimize these difficulties. The proposed approach uses Petri nets and a modular strategy considering shared resources and alternative process planning. Using this approach, a system has been modeled. First the system elements for the modeling job were considered, and then the modules were linked to build the complete model.

A modular modeling approach for CNC machines control using Petri nets

Machine control can be executed in an integrated way using computerized numeric control (CNC) and the programmable logical controller (PLC). CNC deals with axes positioning and speed, the part cut off sequence and the operator interface, while PLC deals with machine interlocking, emergency sequences, start and stop sequences, among others, considering its environment integration. The machine PLC control can be divided into interlocking and sequencing control functions. The article proposes a modular machine PLC control modeling approach using Petri nets. This approach also considers a risk analysis task that defines and classifies the hardware and software interlocking to be implemented to avoid loss and damage. In this approach, the machine PLC control model can be constructed and the Petri net analysis technique can be used to verify and validate it.

An Intelligent Autonomous Vehicle Management System

This paper describes an intelligent vehicle management system, which uses Fuzzy logic to infer, from a set of input variables obtained from the vehicles environment, what is the best destination for the vehicle. This is a decentralized implementation, that is, each vehicle is capable of deciding what is the best destination for itself, and also communicate with other vehicles to see if its objective is the best one globally. To ensure a good response time from the vehicles, without the need of a powerful and expensive hardware, every vehicle uses a table with the output defuzzified values previously calculated for the inference process, instead of a complete fuzzy engine.

A strategy of production scheduling with the fitness function of genetic algorithm using Timed Petri net and considering AGV and the input buffer

Scheduling of machines and AGVs in Flexible Manufacturing Systems involves modeling and searching methodology in a wide solution space. In this work the search for scheduling occurs by genetic algorithm and simple AGV dispatching rules. Modeling occurs in Timed Petri nets in time of fitness evaluation, considering the input buffers of machines, AGVs, and flags also control the use of these buffers, which avoids deadlock. We consider the input buffer of the machines as being of size 1 and allowed to advance transport using them, seeking to optimize the minimum makespan. The proposal was tested in two scenarios of FMS and validated by comparing its results with two others obtained by techniques based on genetic algorithm and adaptive genetic algorithm.

A reactive programming procedure for flexible manufacturing system

Presents an online and reactive system of production scheduling for flexible manufacturing systems. The proposed system simultaneously schedules machining operations and transportation operations through an approach based on fuzzy logic, alternative part-production routes and multi-objective function programming or scheduling. The decision parameters in the decision-making are the routing distance, the due dates of parts and the next operations processing time. A prototype was developed in order to illustrate the advantages of the proposed system.