Emir Buza

Importance of stable velocity in agile maintenance

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.

A hybrid method for prediction of protein secondary structure based on multiple artificial neural networks

The prediction of protein secondary structure is the method of finding the way in which an amino acid sequence causes the protein structure to fold and bend into alpha helices, beta strands and other shapes. Until today, the problem of finding protein secondary structure is not fully resolved. Classification or clusterization based methods have an accuracy rate of circa 80 percent and they mainly work on a reduced set of shapes and folds. It is very difficult to predict how a local sequence of amino acids is going to behave and in which way it is going to affect the future of protein structure. Based upon the predicted secondary structure of the protein, the tertiary and quaternary predictions show the real nature and function of the protein as a whole. In this paper, we address the problem of the secondary structure prediction of protein and propose a new hybrid method based on the usage of multiple neural networks with the use of a consensus function and compare our approach with other efficient methods.

RGB ratios based skin detection

Many different applications like face/people detection, image content interpretation, de-identification for privacy protection in multimedia content, etc. requires skin detection as a pre-processing step. There is no a perfect solution for skin detection, since this process is a compromise on speed, simplicity and precision (detection quality). There are many different techniques for skin detection modeling ranging from simple models based on one or several thresholds to advanced models based on neural network, Bayesian classifier, maximum entropy, k-means clustering, etc. This paper proposes a simple model, based on ratios of red, green and blue components of the RGB color model. It describes how to make a compromise in a skin detection modeling by using three levels of rules. Data analysis that supports conclusions is performed on the dataset from Universidad de Chile (UChile, dbskin2 - complete set) that contains 103 images and their annotations.

Comparation of controllers based on Fuzzy Logic and Artificial Neural Networks for reducing vibration of the driver's seat

This paper presents two approaches in isolation of vibrations of drivers 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.

Pothole detection: An efficient vision based method using RGB color space image segmentation

The proper planning of repairs and rehabilitation of the asphalt pavement is one of the important tasks for safe driving. The most common form of distress on asphalt pavements are potholes, which can compromise safety, and result in vehicle damage. Timely repairing potholes is crucial in ensuring the safety, quality of driving, and reducing the cost of vehicle maintenance. Many of the existing methods for pothole detection often use sophisticated equipment and algorithms, which require substantial amount of data for filtering and training. Consequently, as a result of intensive computational processing, this can lead to long execution time and increased power consumption. In this paper, we propose an efficient unsupervised vision-based method for pothole detection without the process of training and filtering. Our method first extracts asphalt pavements by analysing RGB color space and performing image segmentation. When the asphalt pavement is detected, the search continues in detected region only. The method is tested on online image data set captured from different cameras and angles, with different irregular shapes and number of potholes. The results indicate that the method is suitable as a pre-processing step for other supervised methods.

Customization of software for an unprepared environment and maintaining software stability

Customization of the existing software for a new customer can become problematic if negative factors are not controlled and prevented. This paper discusses customization of software product line (SPL) for an unprepared environment and emphasis importance of identifying this risk factor. The SPL can be viewed as a set of applications for similar environments that share many common elements (processes, interfaces, data models, etc.), but it is important that each instance in the SPL application family is also viewed as a consistent and stabile system that has some specific properties, comparing to others. SPL customization - implementing those specific properties should not endanger stability of the common elements and the system as a whole. Very often preparation for the SPL customization is not as serious as for developing a new software product. That can cause SPL customization for a pretty unknown environment which can be unprepared for accepting such software. Environment that is not willing or not capable to accept (or understand) software in its full potential is named unprepared environment, in this paper. Problems that can arise in an unprepared environment can cause that implemented system is unstable or improperly used, which is described through the case study.