This paper presents two approaches in isolation of vibrations of driver's seat . The first approach shows the ability of Fuzzy Logic Controller (FLC) to adjust the stiffness of the air spring, which is implemented between cabin floor and the seat together with damper in order to isolate the vertical vibrations. The second approach is based on Artificial Neural Network Controller (ANNC) with purpose of improvement vibration isolation producing appropriate voltage for valve flow diameter control of semi-active damper. The quality of isolation is measured using standardized technique. The results of simulation in Matlab/Simulink, as well as the results of implemented controllers on a real experimental model are presented.

Agile maintenance is the best choice if you want to keep step with your customer needs. It is a result of trying to respond to customer change requests with the high efficiency. High involvement of the customer in the maintenance process is good but also can have negative effects. Change in the behavior of the customer can influence the execution of the change management process or cause the change of the release plan, etc. All that can destabilize normal maintenance velocity and lead to a chaotic relationship with the customer, if not controlled or prevented. This paper describes problems in agile maintenance caused mostly by the change of the customer behavior at the beginning of the economic crisis. It also presents results of the analysis of these problems and recommendations how to identify them and how to prevent them.

— Software engineering based on agile methods is different than plan driven in many aspects. Based on our practical experience in agile software engineering we concluded that one of the most important success factors is predicting future change requests. This article emphasizes importance of the future change requests frequency as a very important analysis factor for the later solution selection and software maintenance. It describes a positive experience related to the agile software engineering of the software system for the data import in an environment with frequent change requests, through a case study. The main reason for the success is that the estimation about future changes is taken into account during the analysis. Data import is based on the web service for the XML upload and Oracle database objects for importing, storing and checking data. Meta-model based design is applied to gain flexibility and meet customer’s frequent change requests. A change request is implemented through changing the meta-model parameters which is fast and reliable. There were many change requests through the life of this software system and all of them where low cost changes. Initial higher cost to develop the software that is easy changeable is reimbursed later during the software evolution. Also, changes are implemented fast, with a minimum effort, with the high quality and with the high customer satisfaction.

Pothole detection is one of the important tasks for the proper planning of repairs and rehabilitation of the asphalt-surfaced pavements. Pothole repair is necessary in those situations where potholes compromise safety and pavement ride-ability. Existing methods for detection and estimation of potholes usually use sophisticated equipment and impose computationally intensive tasks. In this paper, we present a new unsupervised vision-based method, which does not require expensive equipment, additional filtering and training phase. Our method deploys image processing and spectral clustering for identification and rough estimation of potholes. Spectral clustering is used for identification of regions with histogram-based data from gray-scaled image. Based on these results, we identify potholes and estimate their surface. Method is tested on images with different pothole shapes and the results show that this method estimates potholes with reasonable accuracy. Key–Words: Pothole detection, Unsupervised method, Spectral clustering, Image processing, Image segmentation, Vision-based approach

This paper presents a novel hybridization of the fuzzy logic, the neural network and the coevolutionary algorithm for building a fuzzy-neural system (or a Mamdani fuzzy system) from data. The novel hybridization uses the coevolution of many species, and proposes the coevolution of groups of similar species, both for the optimization of the structure of the fuzzy-neural network. In the fuzzy-neural network the coevolution changes the number of nodes and their parameters, which indirectly change the number of fuzzy sets and their parameters and the number of rules and their parameters. Specific backpropagation that supports the Mamdani type of fuzzy system is proposed for small size optimization of fuzzy sets parameters. The backpropagation is active while the average absolute error is small, otherwise the backpropagation stops and the coevolution is active. To be able to guide the coevolution based on three criteria the coevolution uses three level of the fitness. It is possible to control the overfitting through these criteria. The proposed hybridization and its Matlab implementation can be used for creating Mamdani fuzzy-neural systems or simply Mamdani fuzzy systems, from data. This is an alternative for ANFIS and similar hybridizations. It offers to users possibility of building a Mamdani fuzzy-neuro system from data, automatically, with optimizing the number of rules, controlling the overfitting, working with large data sets and many variables, using simple triangular fuzzy sets, the result with high function approximation and good knowledge presentation, the result that can be used as the Mamdani fuzzy system (Matlab FIS object) or as the neural network (internal presentation format and feedforward function). This paper also presents the results of testing with the Wisconsin Breast Cancer Database, from UCI Machine Learning Repository ([http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science).

This document proposes an approach for financial statements' anomalies detection by using on-line evolving clustering [1]. Official records of the financial activities of a business are called financial statements and they are recorded in journals and general ledger in a supervised process. Anomalies in financial statements are caused by human mistakes during forming of financial statements, or as a result of changes in the software that produced un-expected errors, or as possible financial fraud.

In this paper we describe CB513 a non-redundant dataset, suitable for development of algorithms for prediction of secondary protein structure. A program was made in Borland Delphi for transforming data from our dataset to make it suitable for learning of neural network for prediction of secondary protein structure implemented in MATLAB Neural-Network Toolbox. Learning (training and testing) of neural network is researched with different sizes of windows, different number of neurons in the hidden layer and different number of training epochs, while using dataset CB513.

—Genetic algorithms are used to solve complex problems in various areas. Research related to genetic algorithms mainly focuses on its three operators: selection, crossover, and mutation. The need to improve the algorithm has led to the creation of different operators out of the three mentioned, many of which are adapted to specific problems. This paper deals with the most commonly used selection operators, and their influence on the efficiency and robustness of the genetic algorithm. The idea behind this paper is to combine selection operators inside the genetic algorithm during its execution to decrease the risk of selecting the inappropriate selection operator for the considered test function. Operators are combined so that preference in the current generation is given to the operator which produces the most suitable population according to the set criteria after crossover and mutation. The criteria used in this paper are the best average overall fitness of the population and the best individual fitness. This research has shown that the change in selection operators within genetic algorithm has positive effects on its functionality.