Samir Omanovic

Evolutionary Approach to Solving Non-stationary Dynamic Multi-Objective Problems

This chapter aims at presenting the general problem of decision making in unknown, complex or changing environment by an extension of static multiobjective optimization problem. General optimization problem is defined, which encompasses not just dynamics, but also change in the optimization problem itself, with focus on changing number of objectives used to evaluate potential solutions.

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.

Comparative analysis of functional and object-oriented programming

The choice of the first programming language and the corresponding programming paradigm is an important part of the software development process. Knowing the advantages and constraints of individual programming paradigms is important as it can be crucial for successful software implementation. In this paper we conduct an empirical comparison of functional and object-oriented programming languages using analog examples in C#, F#, Haskell, and Java. Three algorithms were implemented: algorithm for solving N queens problem, algorithm for generating n-th left-truncatable prime and merge sort algorithm in C#, F#, Haskell and Java programming languages. An overview of programming languages efficiency is given by measuring two basic parameters: number of lines of code and program execution speed. Also, system resource usage is monitored during execution. Limited experiments showed that the programming language Java is faster than the other three languages whose performances were measured. Java was surprisingly fast on these problems that are more suitable for functional programming languages. Haskell was less memory intensive (up to two times less than Java) with similar execution times, while .NET languages were slower up to four times in comparison to Java. Object-oriented languages C# and Java had significantly more lines of code for all three algorithms when compared to functional programming language Haskell and the hybrid one F#.

25th International Conference on Information, Communication, and Automation Technologies [Conference Reports]

Presents information on the 25th International Conference on Information, Communication, and Automation Technologies.

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.

Innovative Approach in Modeling Business Processes with a Focus on Improving the Allocation of Human Resources

Organizations can improve efficiency of process execution through a correct resource allocation, as well as increase income, improve client satisfaction, and so on. This work presents a novel approach for solving problems of resource allocation in business processes which combines process mining, statistical techniques, and metaheuristic algorithms for optimization. In order to get more reliable results of the simulation, in this paper, we use process mining analysis and statistical techniques for building a simulation model. For finding optimal human resource allocation in business processes, we use the improved differential evolution algorithm with population adaptation. Because of the use of a stochastic simulation model, noise appears in the output of the model. The differential evolution algorithm is modified in order to include uncertainty in the fitness function. In the end, validation of the model was done on three different data sets in order to demonstrate the generality of the approach, and the comparison with the standard approach from the literature was done. The results have shown that this novel approach gives solutions which are better than the existing model from literature.

Impact of human resources changes on performance and productivity of Scrum teams

This paper presents results of an analysis of the impact of the human resources changes in Scrum teams. Four Scrum teams were tracked (two developments and two quality assurance) along with their productivity and performance. Analysis showed that human resources changes have a significant impact on the entire team and its behavior. Their effort increased by adding overtime hours. In the same time, their performance and effective work decreased, which is reflected on the quantity of work that can be billed to the client. The analysis shows that it takes, in average, three sprints (each lasting fourteen days) for new team members to fully adjust to the team development process and acquire a business knowledge needed for maximum productivity. Teams whose members have been working together longer period and who have more senior members can adjust to team shifts more quickly. The analysis also showed a correlation between quality assurance and development team - when development team had extra utilization due to overtime, quality assurance team had an increase in overtime hours almost proportionately.

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.

A sequential approach for short-term water level prediction using nonlinear autoregressive neural networks

The water level in an artificial lake is important not only for the production of electric energy but also for other activities such as tourism, irrigation and drought control. The water level in the lake is influenced by various factors, among which the most important include: the water inflow, discharge of water and water seepage. In this research, artificial neural networks (ANN) are selected for the water level prediction because of their well-known abilities for learning from examples. A total of 29 years of water level measurement data was used for ANN training and validation. This paper represents a sequential approach for the short-term water level prediction in Jablanicko lake by using only water level data. With regard to sequential approach for every step of the prediction, the most recent data were used for ANN training. Two types of ANNs were used in this study: Nonlinear Autoregressive (NAR) neural networks and Feed Forward Back Propagation (FFBP) neural networks. The main focus of this study was on NAR networks prediction of water level, while FFBP networks were used for comparison purposes. The results showed that neural networks can provide quality water level prediction even if only water level data is used.