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Your search keyword '"Konjicija, Samim"' showing total 40 results
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40 results on '"Konjicija, Samim"'

1. Prototyping IoT Technology Solutions Using LoRaWAN Infrastructure

Author

Hajdar, Amir, Konjicija, Samim, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Karabegovic, Isak, editor, Kovačević, Ahmed, editor, and Mandzuka, Sadko, editor

Published
2023
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35. Design and implementation of fuzzy controller on embedded computer for water level control.

Author

Krivic, Senka, Hujdur, Muhidin, Mrzic, Aida, and Konjicija, Samim

Abstract

The paper deals with one of frequently encountered tasks in process industry - water level control. Proportional Integral Derivative (PID) control is often used for this purpose. Since control parameters of PID controller are fixed and tank system is inherently nonlinear, PID controller should not be used on wider level range. Therefore, this paper analyzes the effectiveness of water level control using fuzzy controller. The fuzzy controller is implemented based on mathematical model of tank and using MATLAB. The controller is implemented on Friendly ARM - embedded computer. Arduino board is used as an acquisition board for collecting sensor data from tank system Festo Didactic DD 3100 and as a PWM signal generator for water pump control. Experimental results confirm that the fuzzy control system has good adaptability in comparison with PID and provided satisfying results. [ABSTRACT FROM PUBLISHER]

Published
2012

37. Optimized Detection of Tar Content in the Manufacturing Process Using Adaptive Neuro-Fuzzy Inference Systems.

Author

Adlassnig, Klaus-Peter, Blobel, Bernd, Mantas, John, Masic, Izet, Avdagic, Zikrija, Begic Fazlic, Lejla, and Konjicija, Samim

Abstract

The purpose of this study is to model and optimize the detection of tar in cigarettes during the manufacturing process and show that low yield cigarettes contain similar levels of nicotine as compared to high yield cigarettes while B (Benzene), T(toluene) and X (xylene) (BTX) levels increase with increasing tar yields. A neuro-fuzzy system which comprises a fuzzy inference structure is used to model such a system. Given a training set of samples, the Adaptive Neuro-Fuzzy Inference System (ANFIS) classifiers learned how to differentiate a new case in the domain. The ANFIS classifiers were used to detect the tar in smoke condensate when five basic features defining cigarette classes indications were used as inputs. A classical method by High Performance Liquid Chromatography (HPLC) is also introduced to solve this problem. At last the performances of these two methods are compared. [ABSTRACT FROM AUTHOR]

Published
2009

38. Overview of Intelligent Solution for Power Transmission System

Author

Srdjan Skok, Dalibor Brnobic, Igor Ivanković, and Konjicija, Samim

Subjects
Power transmission, Computer science, business.industry, System integrity, 020209 energy, 02 engineering and technology, Grid, Phasor Measurement Unit, Smart Grid, System Integrity Protection Schemes, Synchronized Measurement Technology, Wide Area Monitoring, Protection And Control, Renewable energy, Electric power system, Electric power transmission, Smart grid, 0202 electrical engineering, electronic engineering, information engineering, Systems engineering, Energy market, business
Abstract

The standard of living is in a direct connection to the availability and quality of the energy delivered, and therefore the electric power system presents the backbone for the development of a modern human society. While balancing between the economical and environmental issues, the transmission system operators are coping to maintain the power system stability and integrity. Furthermore, emerging trends such as the integration of intermittent renewable energy sources, electro mobility and the establishing of energy market introduce additional uncertainties and make the power system operation even more complex. The latter has been manifested though the large power system blackouts that have occurred worldwide. However, there has been a shift of attention and funding towards the development and deployment of technologies which would enable the evolutionary transfer of the existing power system towards the intelligent grid - Smart Grid. The synchronized measurement technology (SMT) has already been recognized as one of the key drivers in the building of modern power systems. The various Wide Area Monitoring, Protection And Control (WAMPAC) applications and System Integrity Protection Schemes (SIPS) are issue of this paper that provides a wide overview of the benefits gained by applying the SMT to monitor the operating conditions in the transmission power systems.

Published
2018

39. Alignment of X-ray Bone Images

Author

Mario Mustra, Mislav Grgic, Branka Zovko-Cihlar, Behlilović, Narcis, Hadžialić, Mesud, Konjicija, Samim, and Mrdović, Saša

Subjects
Materials science, business.industry, X-ray, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, bone images, segmentation, alignment, registration, Computer vision, Artificial intelligence, business, ComputingMethodologies_COMPUTERGRAPHICS
Abstract

Switching to digital technology from analog brought many changes, especially in storage of digital X-ray images. In cases of multiple fractures or need for imaging of a large area it is necessary to make more than one X-ray image for the observed part of body. This is often the case with spine, legs and arms. Radiologists use dedicated workstations to view X-ray results and need to manually switch between different images to be able to complete the examination of longer bone segments. Dedicated software gives some possibility of automatic or semiautomatic image stitching which allows combining of multiple images into one. In this paper we propose a method for automatic detection of bone position in X-ray images of arms and legs and their automatic alignment with one of image's borders.

Published
2014

40. Optimized detection of tar content in the manufacturing process using adaptive neuro-fuzzy inference systems.

Author

Avdagic Z, Begic Fazlic L, and Konjicija S

Subjects
Fuzzy Logic, Industry, Tars analysis, Nicotiana chemistry
Abstract

The purpose of this study is to model and optimize the detection of tar in cigarettes during the manufacturing process and show that low yield cigarettes contain similar levels of nicotine as compared to high yield cigarettes while B (Benzene), T(toluene) and X (xylene) (BTX) levels increase with increasing tar yields. A neuro-fuzzy system which comprises a fuzzy inference structure is used to model such a system. Given a training set of samples, the Adaptive Neuro-Fuzzy Inference System (ANFIS) classifiers learned how to differentiate a new case in the domain. The ANFIS classifiers were used to detect the tar in smoke condensate when five basic features defining cigarette classes indications were used as inputs. A classical method by High Performance Liquid Chromatography (HPLC) is also introduced to solve this problem. At last the performances of these two methods are compared.

Published
2009

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