Pavol Tanuska

Toward Nonconventional Human–Machine Interfaces for Supervisory Plant Process Monitoring

This paper describes a novel methodology for designing alternative human-machine interfaces (HMI) dealing with the industrial process control visualization and plant monitoring. The system is based on a multiagent approach in order to allow visualizations using nonconventional display devices (e.g., power-wall or table) combined with the natural user interaction (NUI) paradigm. This type of HMI solution could form an optional extension to current systems employed in a plant monitoring. Namely the utilization of a virtual reality creates new opportunities for the HMI system use cases, where an enhanced visualization and interaction can improve decision making strategies in complex processes. An immersive plant personnel training or realistic process visualizations are examples where the usage of the third dimension can be helpful. Nevertheless, the development of nonconventional HMI increases the complexity of the industrial information system environment. The question is how to adapt the system design for the nonconventional HMI, in a contrast with the conventional solutions. We applied the multiagent approach, which leads to a more robust and less-coupled component structure. An experimental prototype was implemented on the top of the proposed methodology. A simulation of an absorption refrigeration process was used as a process model on top of which the prototype was designed. Interoperability was gained via the automation standard of OPC UA. Furthermore, in order to optimize our NUI agent, a user testing application was developed for the evaluation of exploratory interaction tasks in a power-wall display scenario. The proposed framework provides fundamental guidelines for designing and developing a new generation of HMI systems.

Concept of predictive maintenance of production systems in accordance with industry 4.0

In the proposed paper, we described the approach to build Hadoop based knowledge discovery platform. The proposal focuses on predictive maintenance of production systems, including manufacturing machines and tools, to increase the production process quality. The proposal utilises production data storage, built on Hadoop framework and NoSQL systems, integrated into traditional data warehouse discovery platform, preserving the well proven and robust data warehouse decision support and analytic tools. The initial proof of concept case study is included in the proposed paper.

Data Mining Model Building as a Support for Decision Making in Production Management

The paper gives the next stages of the project that is oriented on the use of data mining techniques and knowledge discoveries from production systems through them. They have been used in the management of these systems. Production data was obtained in previous stages of project. This production data are stored in data warehouse that was proposed and developed by authors. Data mining model has been created by using specific methods and selected techniques for defined problems of production system management. The main focus of our article is the proposal of data mining model.

The Utilization of Metrics Usability to Evaluate the Software Quality

This article discusses about metrics that could be used in the field of software testing. The standard ISO/IEC 9126 was analysed. There is need for change of the design of academic information system at the Faculty of Materials and Science in Trnava as the current one is obsolete. Knowledge about usability metrics has been used for purpose of the proposed new design. By preparing the new application design and testing before being put into service have been saved considerable financial resources that can be invested in further development of the system.

The proposal of functional user requirements generation

This article is aimed on software requirements specification (SRS). At the beginning an introduction to requirement analysis field is provided. After that we point out the place of user requirements in software development life cycle. We are trying to propose basic method for the purpose of reusing Domain Model as basis of requirement generation in order to save initial time for defining user requirements in software engineering industry.

The proposal of ontology as a part of University data warehouse

Main goal of this paper is the proposal of modified university ontology as a part of University data warehouse. The existing ontology does not include all the required concepts and information that are necessary in the evaluation process. In a first step, university ontology is needed to define the base classes that become entities and their associations. After design phase and verification of class diagrams that present our proposed ontology, the mapping into the Data Warehouse multidimensional Star Schema is presented. The most valuable contribution (after implementation phase) will be the possibility to online evaluate the study achievements of students either individually or globally for a departments and faculties according to various parameters, which will undoubtedly increase the quality of pedagogical process. And it should decrease student failure rates.

Creating a management view on key indicators using business intelligence in small and medium enterprises

Business Intelligence is new important area of informatics. It presents options to work with data and provides basic theoretical determination of the issue. Business Intelligence as a process of transforming data into information and knowledge is one of the fastest growing areas of information technology. We want to deepen knowledge and skills and show how we can work with data and acquired important information concerning business easy and fast.

Prediction of Selected Production Goals by Classification Methods

The goal of this work was to use the process of knowledge discovery in planning and control of production processes. This work is focused on the prediction of the system behavior from the data of production process. The classification was used as a task of data mining. Some predictive models were created and the predictions of the production process behavior were realized by varying the input parameters using selected methods and techniques of data mining. It can be confirmed that the selected input parameters will lead to the fulfillment of the declared objectives of the process. The process of knowledge discovery has been implemented in the program STATISTICA Data Miner.

Duffing-Type Oscillator with a Bounded from above Potential in the Presence of Saddle-Center Bifurcation and Singular Perturbation: Frequency Control

We analyze the dynamics of the forced singularly perturbed differential equations of Duffing’s type with a potential that is bounded from above. We explain the appearance of the large frequency nonlinear oscillations of the solutions. It is shown that the frequency can be controlled by a small parameter at the highest derivative.

Pattern Recognition for Predictive Analysis in Automotive Industry

Predictive maintenance (PdM) techniques are designed to help identify the condition of devices in order to predict when maintenance should be performed. The ultimate goal of PdM is to perform maintenance at a scheduled point in time when the maintenance activity is most cost-effective and before the equipment loses performance within a threshold. Currently, reducing service costs and losses due to downtime is one of the ways to increase your profits and success in the market. We tried to identify problem messages and failures from the manufacturing data example set from car body work. Two different data sets were joined and we designed a process to identify message and failure alerts preceding errors.