Marcin Gabryel

On Applying Evolutionary Computation Methods to Optimization of Vacation Cycle Costs in Finite-Buffer Queue

In this paper, problem of positioning and optimization of operation costs for finite-buffer queuing system with exponentially distributed server vacation is investigated. The problem is solved using evolutionary computation methods for independent 2-order hyper exponential input stream of packets and exponential service time distribution. Different scenarios of system operation are analyzed, i.e. different values of parameters of distribution functions describing evolution of the system.

From Single Image to List of Objects Based on Edge and Blob Detection

In this paper we present a new method for obtaining a list of interest objects from a single image. Our object extraction method works on two well known algorithms: the Canny edge detection method and the quadrilaterals detection. Our approach allows to select only the significant elements of the image. In addition, this method allows to filter out unnecessary key points in a simple way (for example obtained by the SIFT algorithm) from the background image. The effectiveness of the method is confirmed by experimental research.

Creating learning sets for control systems using an evolutionary method

The acquisition of the knowledge which is useful for developing of artificial intelligence systems is still a problem. We usually ask experts, apply historical data or reap the results of mensuration from a real simulation of the object. In the paper we propose a new algorithm to generate a representative training set. The algorithm is based on analytical or discrete model of the object with applied the k---nn and genetic algorithms. In this paper it is presented the control case of the issue illustrated by well known truck backer---upper problem. The obtained training set can be used for training many AI systems such as neural networks, fuzzy and neuro---fuzzy architectures and k---nn systems.

A finite-buffer queue with a single vacation policy

Abstract In this paper, application of an evolutionary strategy to positioning a GI/M/1/N-type finite-buffer queueing system with exhaustive service and a single vacation policy is presented. The examined object is modeled by a conditional joint transform of the first busy period, the first idle time and the number of packets completely served during the first busy period. A mathematical model is defined recursively by means of input distributions. In the paper, an analytical study and numerical experiments are presented. A cost optimization problem is solved using an evolutionary strategy for a class of queueing systems described by exponential and Erlang distributions.

Genetic Cost Optimization of the GI/M/1/N Finite-Buffer Queue with a Single Vacation Policy

In the artice, problem of the cost optimization of the GI/M/1/N-type queue with finite buffer and a single vacation policy is analyzed. Basing on the explicit representation for the joint transform of the first busy period, first idle time and the number of packets transmitted during the first busy period and fixed values of unit costs of the server’s functioning an optimal set of system parameters is found for exponentially distributed vacation period and 2-Erlang distribution of inter arrival times. The problem of optimization is solved using genetic algorithm. Different variants of the load of the system are considered as well.

Content-Based Image Indexing by Data Clustering and Inverse Document Frequency

In this paper we present an algorithm for creating and searching large image databases. Effective browsing and searching such collections of images based on their content is one of the most important challenges of computer science. In the presented algorithm, the process of inserting data to the database consists of several stages. In the first step interest points are generated from images by e.g. SIFT, SURF or PCA SIFT algorithms. The resulting huge number of key points is then reduced by data clustering, in our case by a novel, parameterless version of the mean shift algorithm. The reduction is achieved by subsequent operation on generated cluster centers. This algorithm has been adapted specifically for the presented method. Cluster centers are treated as terms and images as documents in the term frequency-inverse document frequency (TF-IDF) algorithm. TF-IDF algorithm allows to create an indexed image database and to fast retrieve desired images. The proposed approach is validated by numerical experiments on images with different content.

Modified Merge Sort Algorithm for Large Scale Data Sets

Sorting algorithms find their application in many fields. One of their main uses is to organize databases. Classical applications of sorting algorithms often can not cope satisfactorily with large data sets or with unfavorable poses of sorted strings. Typically, in such situations, we try to use other methods or apply sorting process to reshuffled input data. Unfortunately, this approach complicates sorting process and often results in significant prolongation of the time. In this paper, the authors examined an algorithm dedicated to the problem of sorting large scale data sets. In the literature, there are no studies of such examples. These studies will allow to describe the properties of sorting methods for large scale data sets. Performed tests have shown superior performance of the examined algorithm, especially for large scale data sets. Changes sped up sorting of data with any arrangement of the input elements.

Object Detection by Simple Fuzzy Classifiers Generated by Boosting

Finding key points based on SURF and SIFT and size of their vector reduction is a classical approach for object recognition systems. In this paper we present a new framework for object recognition based on generating simple fuzzy classifiers using key points and boosting meta learning to distinguish between one known class and other classes. We tested proposed approach on a known image dataset.

An Application of Differential Evolution to Positioning Queueing Systems

In this paper, we present positioning of the queueing system by the use of Differential Evolution algorithm. Positioned system is a H 3/GI/M/1/N-type queueing model with exponentially distributed service and vacation. In the following sections of this article we discuss the possibility of positioning of the selected system in various common scenarios of operation, which are modeled with the independent 3-order hyper exponential input stream of packets and exponential service time distribution. The research results on positioning are presented and discussed to show potential benefits of applied optimization method.

Image Indexing and Retrieval Using GSOM Algorithm

Growing Self Organized Map (GSOM) algorithm is a well-known unsupervised clustering algorithm which a definite advantage is that both the map structure as well as the number of classes are automatically adjusted depending on the training data. We propose a new approach to apply it in the process of the image indexation and retrieval in a database. Unlike the classic bag-of-words (BoW) algorithm with k-means clustering, it is completely unnecessary to predetermine the number of classes (words). Thanks to that, the process of indexation can be fully automated. What is more, numerous modifications of the classic algorithm were added, and as a result, the retrieval process was considerably improved. Results of the experiments as well as comparison with BoW are presented at the end of the paper.