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Kamil Židek
Technical University of Košice

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Conference paper
Published: 03 August 2021 in 5th EAI International Conference on Management of Manufacturing Systems
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The paper presents a new approach to recognize small parts from high-resolution 4K images grabbed by the embedded camera with trained convolutional network models. The main principle is a combination of standard machine vision techniques (OpenCV library) to identify blobs in the high-resolution image and parse to a suitable set of regions of interest, which are later recognized by trained convolutional neural network model (Tensorflow framework). The convolutional neural network model with single-shot detection was selected to identify the position of the object. 3D virtual models of parts with autogenerated orientations are used for simpler input data generation. Two embedded devices were selected for use in the experimental system. The first is NVidia Xavier dev board with 4K camera and integrated APU and the second is Raspberry PI 4 with external APU Intel Neural Compute Stick 2. The last part of the paper shows the implementation of the presented principle to the virtual device (HTC Vive Pro) for assisted assembly tasks to train and check workers during the teaching process.

ACS Style

Kamil Židek; Ján Piteľ; Ivan Pavlenko; Peter Lazorík; Alexander Hošovský. Small Parts Recognition by Convolutional Neural Networks with Implementation to Virtual Reality Devices for Assisted Assembly Tasks. 5th EAI International Conference on Management of Manufacturing Systems 2021, 185 -196.

AMA Style

Kamil Židek, Ján Piteľ, Ivan Pavlenko, Peter Lazorík, Alexander Hošovský. Small Parts Recognition by Convolutional Neural Networks with Implementation to Virtual Reality Devices for Assisted Assembly Tasks. 5th EAI International Conference on Management of Manufacturing Systems. 2021; ():185-196.

Chicago/Turabian Style

Kamil Židek; Ján Piteľ; Ivan Pavlenko; Peter Lazorík; Alexander Hošovský. 2021. "Small Parts Recognition by Convolutional Neural Networks with Implementation to Virtual Reality Devices for Assisted Assembly Tasks." 5th EAI International Conference on Management of Manufacturing Systems , no. : 185-196.

Conference paper
Published: 20 June 2021 in Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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The paper describes design of smart production system with full digitalization for requirements of Industry 4.0. The system consists of three level of technologies: production technologies, inspection subsystem and digitalization software system. Production technologies creates parts for assembly: small CNC, Rapid prototyping device and standardized parts storage with assisted assembly process by collaborative robot combined with mixed reality device. This system provides assisted assembly work cell. Inspection subsystems consist of RFID readers with passive tags, vision system with basic 3D inspection, multi-spectrum light for error detection and 3D profilometer precise measuring. Digitalization software is represented as Digital Twin model implemented to server, which uses OPC communication for data transfer from production system to Cloud Platform with additional data from IoT devices.

ACS Style

Kamil Židek; Vratislav Hladký; Ján Pitel’; Jakub Demčák; Alexander Hošovský; Peter Lazorík. SMART Production System with Full Digitalization for Assembly and Inspection in Concept of Industry 4.0. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2021, 181 -192.

AMA Style

Kamil Židek, Vratislav Hladký, Ján Pitel’, Jakub Demčák, Alexander Hošovský, Peter Lazorík. SMART Production System with Full Digitalization for Assembly and Inspection in Concept of Industry 4.0. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. 2021; ():181-192.

Chicago/Turabian Style

Kamil Židek; Vratislav Hladký; Ján Pitel’; Jakub Demčák; Alexander Hošovský; Peter Lazorík. 2021. "SMART Production System with Full Digitalization for Assembly and Inspection in Concept of Industry 4.0." Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering , no. : 181-192.

Journal article
Published: 08 May 2021 in Applied Sciences
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The assisted assembly of customized products supported by collaborative robots combined with mixed reality devices is the current trend in the Industry 4.0 concept. This article introduces an experimental work cell with the implementation of the assisted assembly process for customized cam switches as a case study. The research is aimed to design a methodology for this complex task with full digitalization and transformation data to digital twin models from all vision systems. Recognition of position and orientation of assembled parts during manual assembly are marked and checked by convolutional neural network (CNN) model. Training of CNN was based on a new approach using virtual training samples with single shot detection and instance segmentation. The trained CNN model was transferred to an embedded artificial processing unit with a high-resolution camera sensor. The embedded device redistributes data with parts detected position and orientation into mixed reality devices and collaborative robot. This approach to assisted assembly using mixed reality, collaborative robot, vision systems, and CNN models can significantly decrease assembly and training time in real production.

ACS Style

Kamil Židek; Ján Piteľ; Michal Balog; Alexander Hošovský; Vratislav Hladký; Peter Lazorík; Angelina Iakovets; Jakub Demčák. CNN Training Using 3D Virtual Models for Assisted Assembly with Mixed Reality and Collaborative Robots. Applied Sciences 2021, 11, 4269 .

AMA Style

Kamil Židek, Ján Piteľ, Michal Balog, Alexander Hošovský, Vratislav Hladký, Peter Lazorík, Angelina Iakovets, Jakub Demčák. CNN Training Using 3D Virtual Models for Assisted Assembly with Mixed Reality and Collaborative Robots. Applied Sciences. 2021; 11 (9):4269.

Chicago/Turabian Style

Kamil Židek; Ján Piteľ; Michal Balog; Alexander Hošovský; Vratislav Hladký; Peter Lazorík; Angelina Iakovets; Jakub Demčák. 2021. "CNN Training Using 3D Virtual Models for Assisted Assembly with Mixed Reality and Collaborative Robots." Applied Sciences 11, no. 9: 4269.

Journal article
Published: 02 November 2020 in Journal of Building Engineering
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Forecasting energy consumption in buildings is crucial for achieving effective energy management as well as reducing environmental impacts. With the availability of large amounts of relevant data through smart metering, gas consumption forecasting is becoming an integral part of smart building design so that these requirements are met. In this study, we investigate week-ahead forecasting of daily gas consumption in three types of buildings characterized by different gas consumption profiles during a five-year period. As gas consumption in buildings is highly correlated with the average outdoor temperature, regression models with additional residual modeling are used for forecasting. However, conventional regression models with autoregressive moving averages (ARMA) errors (regARMA) perform poorly when the temperature forecasts are inaccurate. To address this, a new forecasting model termed genetic-algorithm-optimized regression wavelet neural network (GA-optimized regWANN) is proposed. It uses the wavelet decomposition of the residuals of temperature regression time-series, which are modeled by multiple nonlinear autoregressive (NAR) models based on sigmoid neural networks. The appropriate delays in the regression vectors of the NAR models are selected using a binary GA. Compared with regARMA and seasonal regARMA, the GA-optimized regWANN model achieved in the three buildings a reduction of 22.6%, 17.7%, and 57% in the mean absolute error (MAE) values in ex post forecasting with recorded temperatures, and a 52.5%, 27%, and 43.6% reduction in the MAE values in ex ante forecasting with week-ahead forecasted temperatures, even under conditions of relatively significant errors in the forecasted temperature.

ACS Style

Alexander Hošovský; Ján Piteľ; Milan Adámek; Jana Mižáková; Kamil Židek. Comparative study of week-ahead forecasting of daily gas consumption in buildings using regression ARMA/SARMA and genetic-algorithm-optimized regression wavelet neural network models. Journal of Building Engineering 2020, 34, 101955 .

AMA Style

Alexander Hošovský, Ján Piteľ, Milan Adámek, Jana Mižáková, Kamil Židek. Comparative study of week-ahead forecasting of daily gas consumption in buildings using regression ARMA/SARMA and genetic-algorithm-optimized regression wavelet neural network models. Journal of Building Engineering. 2020; 34 ():101955.

Chicago/Turabian Style

Alexander Hošovský; Ján Piteľ; Milan Adámek; Jana Mižáková; Kamil Židek. 2020. "Comparative study of week-ahead forecasting of daily gas consumption in buildings using regression ARMA/SARMA and genetic-algorithm-optimized regression wavelet neural network models." Journal of Building Engineering 34, no. : 101955.

Journal article
Published: 07 October 2020 in MM Science Journal
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The paper deals with the design of an automated measuring and error detection system integrated into the turning machine. The experimental measuring device is based on a laser sensor with data transfer to the PLC control system. The main advantage of the designed experimental system is the possib...

ACS Style

Kamil Zidek; Jan Pitel; Alexander Hosovsky; Natalia Lishchenko; Martin Miskiv-Pavlik; Peter Lazorik; Jozef Zbihej. AN AUTOMATIC ERROR SURFACE DIAGNOSTICS DURING TURNING MACHINING OPERATION USING LASER SENSOR. MM Science Journal 2020, 2020, 3995 -3999.

AMA Style

Kamil Zidek, Jan Pitel, Alexander Hosovsky, Natalia Lishchenko, Martin Miskiv-Pavlik, Peter Lazorik, Jozef Zbihej. AN AUTOMATIC ERROR SURFACE DIAGNOSTICS DURING TURNING MACHINING OPERATION USING LASER SENSOR. MM Science Journal. 2020; 2020 (3):3995-3999.

Chicago/Turabian Style

Kamil Zidek; Jan Pitel; Alexander Hosovsky; Natalia Lishchenko; Martin Miskiv-Pavlik; Peter Lazorik; Jozef Zbihej. 2020. "AN AUTOMATIC ERROR SURFACE DIAGNOSTICS DURING TURNING MACHINING OPERATION USING LASER SENSOR." MM Science Journal 2020, no. 3: 3995-3999.

Chapter
Published: 01 July 2020 in Advanced Controllers for Smart Cities
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This paper describes a new methodology for hybrid data collection from IoT (internet of things) devices by three separate technologies: GSM (Global System for Mobile Communications) (LTE-M/NB-IoT (Long-Term Evolution for Machines/Narrowband Internet of Things)), Sigfox, and LoRa/LoRaWAN. We discuss experiments involving event-based switching with actual monitoring of the different IoT communication technologies’ signal quality. The experiments demonstrated that combination of IoT technology can provide stable data flow from a production process to cloud platform systems. We tested the signal quality in a highly urbanized environment and a more sparsely inhabited environment by monitoring errors during travel between two towns. The next area of study is the problem of connection and transformation of digitalized data to a cloud platform with adequate synchronization. The tested open-source cloud platform was built on an InfluxDB database using the Grafana visualization framework. Data from industrial devices—for example, RFID (radiofrequency identification) and vision—can be transferred by a local network with an IoT gateway and an OPC (Open Platform Communications) server to commercial cloud platforms.

ACS Style

Kamil Židek; Ján Piteľ; Peter Lazorík. IoT System with Switchable GSM, LoRaWAN, and Sigfox Communication Technology for Reliable Data Collection to Open-Source or Industrial Cloud Platforms. Advanced Controllers for Smart Cities 2020, 311 -333.

AMA Style

Kamil Židek, Ján Piteľ, Peter Lazorík. IoT System with Switchable GSM, LoRaWAN, and Sigfox Communication Technology for Reliable Data Collection to Open-Source or Industrial Cloud Platforms. Advanced Controllers for Smart Cities. 2020; ():311-333.

Chicago/Turabian Style

Kamil Židek; Ján Piteľ; Peter Lazorík. 2020. "IoT System with Switchable GSM, LoRaWAN, and Sigfox Communication Technology for Reliable Data Collection to Open-Source or Industrial Cloud Platforms." Advanced Controllers for Smart Cities , no. : 311-333.

Journal article
Published: 11 May 2020 in Processes
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This article was aimed to solve an urgent problem of ensuring quality for prilling processes in vibrational prilling equipment. During the research, the need for the application of vibrational prilling to create a controlled impact on the process of jet decay on droplets with the proper characteristics was substantiated. Based on the experimental and theoretical studies of the process of decay of a liquid jet into drops, axisymmetric droplet oscillation modes for the different frequencies were observed. Frequency ranges of transition between modes of decay of a jet into drops were obtained. As a result, the mathematical model of the droplet deformation was refined. The experimental research data substantiated this model, and its implementation allowed determining the analytical dependencies for the components of the droplet deformation velocity. The proposed model explains the existence of different droplet oscillation modes depending on the frequency characteristics of the superimposed vibrational impact. Based on an analytical study of the droplet deformation velocity components, the limit values of the characteristics defining the transition between the different droplet oscillation modes were discovered. Analytical dependencies were also obtained to determine the diameter of the satellites and their total number.

ACS Style

Ivan Pavlenko; Vsevolod Sklabinskyi; Ján Piteľ; Kamil Židek; Ivan Kuric; Vitalii Ivanov; Maksym Skydanenko; Oleksandr Liaposhchenko. Effect of Superimposed Vibrations on Droplet Oscillation Modes in Prilling Process. Processes 2020, 8, 566 .

AMA Style

Ivan Pavlenko, Vsevolod Sklabinskyi, Ján Piteľ, Kamil Židek, Ivan Kuric, Vitalii Ivanov, Maksym Skydanenko, Oleksandr Liaposhchenko. Effect of Superimposed Vibrations on Droplet Oscillation Modes in Prilling Process. Processes. 2020; 8 (5):566.

Chicago/Turabian Style

Ivan Pavlenko; Vsevolod Sklabinskyi; Ján Piteľ; Kamil Židek; Ivan Kuric; Vitalii Ivanov; Maksym Skydanenko; Oleksandr Liaposhchenko. 2020. "Effect of Superimposed Vibrations on Droplet Oscillation Modes in Prilling Process." Processes 8, no. 5: 566.

Journal article
Published: 01 May 2020 in Sustainability
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This article deals with the creation of a digital twin for an experimental assembly system based on a belt conveyor system and an automatized line for quality production check. The point of interest is a Bowden holder assembly from a 3D printer, which consists of a stepper motor, plastic components, and some fastener parts. The assembly was positioned in a fixture with ultra high frequency (UHF) tags and internet of things (IoT) devices for identification of status and position. The main task was parts identification and inspection, with the synchronization of all data to a digital twin model. The inspection system consisted of an industrial vision system for dimension, part presence, and errors check before and after assembly operation. A digital twin is realized as a 3D model, created in CAD design software (CDS) and imported to a Tecnomatix platform to simulate all processes. Data from the assembly system were collected by a programmable logic controller (PLC) system and were synchronized by an open platform communications (OPC) server to a digital twin model and a cloud platform (CP). Digital twins can visualize the real status of a manufacturing system as 3D simulation with real time actualization. Cloud platforms are used for data mining and knowledge representation in timeline graphs, with some alarms and automatized protocol generation. Virtual digital twins can be used for online optimization of an assembly process without the necessity to stop that is involved in a production line.

ACS Style

Kamil Židek; Ján Piteľ; Milan Adámek; Peter Lazorík; Alexander Hošovský. Digital Twin of Experimental Smart Manufacturing Assembly System for Industry 4.0 Concept. Sustainability 2020, 12, 3658 .

AMA Style

Kamil Židek, Ján Piteľ, Milan Adámek, Peter Lazorík, Alexander Hošovský. Digital Twin of Experimental Smart Manufacturing Assembly System for Industry 4.0 Concept. Sustainability. 2020; 12 (9):3658.

Chicago/Turabian Style

Kamil Židek; Ján Piteľ; Milan Adámek; Peter Lazorík; Alexander Hošovský. 2020. "Digital Twin of Experimental Smart Manufacturing Assembly System for Industry 4.0 Concept." Sustainability 12, no. 9: 3658.

Conference paper
Published: 04 March 2020 in International Conference on Mobile Computing and Sustainable Informatics
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This chapter deals with implementation of digital twin for experimental quality control system to remote monitoring, simulation, and optimization of real process. The main area of study is problem of connection and transformation of digital data from quality control process and product to digital twins and synchronization with Cloud Platform. Actual status of experimental quality control system is synchronized with digital twin for online interaction. Digitalized data must be stored in long-term horizon, which is performed by Cloud Platform, and it provides Big Data processing techniques. Digital twin of quality control system is transferred from 3D model to simulation software Tecnomatix. Interconnection between cloud control system and simulated Tecnomatix model (digital twin) is realized by OPC server. The technologies selected for data collection from experimental system are vision systems, RFID, and MEMS devices.

ACS Style

Kamil Zidek; Jan Pitel; Ivan Pavlenko; Peter Lazorik; Alexander Hosovsky. Digital Twin of Experimental Workplace for Quality Control with Cloud Platform Support. International Conference on Mobile Computing and Sustainable Informatics 2020, 135 -145.

AMA Style

Kamil Zidek, Jan Pitel, Ivan Pavlenko, Peter Lazorik, Alexander Hosovsky. Digital Twin of Experimental Workplace for Quality Control with Cloud Platform Support. International Conference on Mobile Computing and Sustainable Informatics. 2020; ():135-145.

Chicago/Turabian Style

Kamil Zidek; Jan Pitel; Ivan Pavlenko; Peter Lazorik; Alexander Hosovsky. 2020. "Digital Twin of Experimental Workplace for Quality Control with Cloud Platform Support." International Conference on Mobile Computing and Sustainable Informatics , no. : 135-145.

Chapter
Published: 03 January 2020 in Industry 4.0 for SMEs
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This chapter deals with an implementation of advanced vision technologies for contactless parts of product inspection and automatic object identification using RFID during the assembly process in experimental assembly line to improve quality control over the assembly of the different product parts. Both, vision technologies and UHF RFID system are used for digitization of quality control, and automatic identification for the future world of the Internet of Things (IoT). Moreover, all quality control data are stored in Cloud Platform for the purpose of data analysis and visualization. Subsequently, digital twin of quality control system is generated from its 3D model and transformed into virtual reality device for remote monitoring of quality control.

ACS Style

Kamil Židek; Vladimír Modrák; Ján Pitel; Zuzana Šoltysová. The Digitization of Quality Control Operations with Cloud Platform Computing Technologies. Industry 4.0 for SMEs 2020, 305 -334.

AMA Style

Kamil Židek, Vladimír Modrák, Ján Pitel, Zuzana Šoltysová. The Digitization of Quality Control Operations with Cloud Platform Computing Technologies. Industry 4.0 for SMEs. 2020; ():305-334.

Chicago/Turabian Style

Kamil Židek; Vladimír Modrák; Ján Pitel; Zuzana Šoltysová. 2020. "The Digitization of Quality Control Operations with Cloud Platform Computing Technologies." Industry 4.0 for SMEs , no. : 305-334.

Conference paper
Published: 26 April 2019 in Proceedings of the 2nd Annual International Conference on Material, Machines and Methods for Sustainable Development (MMMS2020)
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One of the most monotonous activities in using convolutional neural networks for image recognition is preparation of the learning data. It involves creating samples (2D images of object) at different angles of view, different backgrounds/materials and partial overlay of the object. Input data must include a relatively large number of frames, typically about 100 and more images per object to make the learning precision useful. In the paper there is proposed a new approach to creating these data fully automated based on a virtual 3D model of the standardized parts. Automation principle is generating 2D images from the imported 3D construction model, including the following variable parameters: the angle of rotation, background and the material of the component. We used for verification pretrained DNN model Faster RCNN Inception v2 with single shot detection (SSD). The learned convolutional network was next tested by real samples to verify a new approach of learning by virtual models and recognition of real objects (parts).

ACS Style

Kamil Židek; Peter Lazorík; Ján Piteľ; Ivan Pavlenko; Alexander Hošovský. Automated Training of Convolutional Networks by Virtual 3D Models for Parts Recognition in Assembly Process. Proceedings of the 2nd Annual International Conference on Material, Machines and Methods for Sustainable Development (MMMS2020) 2019, 287 -297.

AMA Style

Kamil Židek, Peter Lazorík, Ján Piteľ, Ivan Pavlenko, Alexander Hošovský. Automated Training of Convolutional Networks by Virtual 3D Models for Parts Recognition in Assembly Process. Proceedings of the 2nd Annual International Conference on Material, Machines and Methods for Sustainable Development (MMMS2020). 2019; ():287-297.

Chicago/Turabian Style

Kamil Židek; Peter Lazorík; Ján Piteľ; Ivan Pavlenko; Alexander Hošovský. 2019. "Automated Training of Convolutional Networks by Virtual 3D Models for Parts Recognition in Assembly Process." Proceedings of the 2nd Annual International Conference on Material, Machines and Methods for Sustainable Development (MMMS2020) , no. : 287-297.

Journal article
Published: 05 April 2019 in Symmetry
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Small series production with a high level of variability is not suitable for full automation. So, a manual assembly process must be used, which can be improved by cooperative robots and assisted by augmented reality devices. The assisted assembly process needs reliable object recognition implementation. Currently used technologies with markers do not work reliably with objects without distinctive texture, for example, screws, nuts, and washers (single colored parts). The methodology presented in the paper introduces a new approach to object detection using deep learning networks trained remotely by 3D virtual models. Remote web application generates training input datasets from virtual 3D models. This new approach was evaluated by two different neural network models (Faster RCNN Inception v2 with SSD, MobileNet V2 with SSD). The main advantage of this approach is the very fast preparation of the 2D sample training dataset from virtual 3D models. The whole process can run in Cloud. The experiments were conducted with standard parts (nuts, screws, washers) and the recognition precision achieved was comparable with training by real samples. The learned models were tested by two different embedded devices with an Android operating system: Virtual Reality (VR) glasses, Cardboard (Samsung S7), and Augmented Reality (AR) smart glasses (Epson Moverio M350). The recognition processing delays of the learned models running in embedded devices based on an ARM processor and standard x86 processing unit were also tested for performance comparison.

ACS Style

Kamil Židek; Peter Lazorík; Ján Piteľ; Alexander Hošovský. An Automated Training of Deep Learning Networks by 3D Virtual Models for Object Recognition. Symmetry 2019, 11, 496 .

AMA Style

Kamil Židek, Peter Lazorík, Ján Piteľ, Alexander Hošovský. An Automated Training of Deep Learning Networks by 3D Virtual Models for Object Recognition. Symmetry. 2019; 11 (4):496.

Chicago/Turabian Style

Kamil Židek; Peter Lazorík; Ján Piteľ; Alexander Hošovský. 2019. "An Automated Training of Deep Learning Networks by 3D Virtual Models for Object Recognition." Symmetry 11, no. 4: 496.

Journal article
Published: 12 December 2018 in MM Science Journal
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ACS Style

Alexander Hosovsky; Ján Piteľ; Jana Mizakova; Kamil Zidek. INTRODUCTORY ANALYSIS OF GAS CONSUMPTION TIME SERIES IN NON-RESIDENTIAL BUILDINGS FOR PREDICTION PURPOSES USING WAVELET DECOMPOSITION. MM Science Journal 2018, 12, 2648 -2655.

AMA Style

Alexander Hosovsky, Ján Piteľ, Jana Mizakova, Kamil Zidek. INTRODUCTORY ANALYSIS OF GAS CONSUMPTION TIME SERIES IN NON-RESIDENTIAL BUILDINGS FOR PREDICTION PURPOSES USING WAVELET DECOMPOSITION. MM Science Journal. 2018; 12 (2018):2648-2655.

Chicago/Turabian Style

Alexander Hosovsky; Ján Piteľ; Jana Mizakova; Kamil Zidek. 2018. "INTRODUCTORY ANALYSIS OF GAS CONSUMPTION TIME SERIES IN NON-RESIDENTIAL BUILDINGS FOR PREDICTION PURPOSES USING WAVELET DECOMPOSITION." MM Science Journal 12, no. 2018: 2648-2655.

Conference paper
Published: 15 September 2018 in Proceedings of the 2nd Annual International Conference on Material, Machines and Methods for Sustainable Development (MMMS2020)
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The paper describes the experiments with the use of deep neural networks (CNN) for robust identification of assembly parts (screws, nuts) and assembly features (holes), to speed up any assembly process with augmented reality application. The simple image processing tasks with static camera and recognized parts can be handled by standard image processing algorithms (threshold, Hough line/circle detection and contour detection), but the augmented reality devices require dynamic recognition of features detected in various distances and angles. The problem can be solved by deep learning CNN which is robust to orientation, scale and in cases when element is not fully visible. We tested two pretrained CNN models Mobilenet V1 and SSD Fast RCNN Inception V2 SSD extension have been tested to detect exact position. The results obtained were very promising in comparison to standard image processing techniques.

ACS Style

Kamil Židek; Alexander Hosovsky; Jan Piteľ; Slavomír Bednár. Recognition of Assembly Parts by Convolutional Neural Networks. Proceedings of the 2nd Annual International Conference on Material, Machines and Methods for Sustainable Development (MMMS2020) 2018, 281 -289.

AMA Style

Kamil Židek, Alexander Hosovsky, Jan Piteľ, Slavomír Bednár. Recognition of Assembly Parts by Convolutional Neural Networks. Proceedings of the 2nd Annual International Conference on Material, Machines and Methods for Sustainable Development (MMMS2020). 2018; ():281-289.

Chicago/Turabian Style

Kamil Židek; Alexander Hosovsky; Jan Piteľ; Slavomír Bednár. 2018. "Recognition of Assembly Parts by Convolutional Neural Networks." Proceedings of the 2nd Annual International Conference on Material, Machines and Methods for Sustainable Development (MMMS2020) , no. : 281-289.

Journal article
Published: 07 March 2018 in MM Science Journal
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ACS Style

Kamil Zidek; Vladimir Vasek; Jan Pitel; Alexander Hošovský. AUXILIARY DEVICE FOR ACCURATE MEASUREMENT BY THE SMART VISION SYSTEM. MM Science Journal 2018, 2018, 2136 -2139.

AMA Style

Kamil Zidek, Vladimir Vasek, Jan Pitel, Alexander Hošovský. AUXILIARY DEVICE FOR ACCURATE MEASUREMENT BY THE SMART VISION SYSTEM. MM Science Journal. 2018; 2018 (1):2136-2139.

Chicago/Turabian Style

Kamil Zidek; Vladimir Vasek; Jan Pitel; Alexander Hošovský. 2018. "AUXILIARY DEVICE FOR ACCURATE MEASUREMENT BY THE SMART VISION SYSTEM." MM Science Journal 2018, no. 1: 2136-2139.

Conference paper
Published: 01 January 2018 in Proceedings of the 3rd EAI International Conference on Management of Manufacturing Systems
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ACS Style

Kamil Zidek; Dagmar Janacova; Alexander Hosovsky; Jan Pitel; Peter Lazorik. Data optimization for communication between wireless IoT devices and Cloud platforms in production process. Proceedings of the 3rd EAI International Conference on Management of Manufacturing Systems 2018, 1 .

AMA Style

Kamil Zidek, Dagmar Janacova, Alexander Hosovsky, Jan Pitel, Peter Lazorik. Data optimization for communication between wireless IoT devices and Cloud platforms in production process. Proceedings of the 3rd EAI International Conference on Management of Manufacturing Systems. 2018; ():1.

Chicago/Turabian Style

Kamil Zidek; Dagmar Janacova; Alexander Hosovsky; Jan Pitel; Peter Lazorik. 2018. "Data optimization for communication between wireless IoT devices and Cloud platforms in production process." Proceedings of the 3rd EAI International Conference on Management of Manufacturing Systems , no. : 1.

Journal article
Published: 07 September 2016 in MM Science Journal
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ACS Style

Alexander Hosovsky; Jan Pitel; Kamil Zidek. ANALYSIS OF HYSTERETIC BEHAVIOR OF TWO-DOF SOFT ROBOTIC ARM. MM Science Journal 2016, 2016, 935 -941.

AMA Style

Alexander Hosovsky, Jan Pitel, Kamil Zidek. ANALYSIS OF HYSTERETIC BEHAVIOR OF TWO-DOF SOFT ROBOTIC ARM. MM Science Journal. 2016; 2016 (3):935-941.

Chicago/Turabian Style

Alexander Hosovsky; Jan Pitel; Kamil Zidek. 2016. "ANALYSIS OF HYSTERETIC BEHAVIOR OF TWO-DOF SOFT ROBOTIC ARM." MM Science Journal 2016, no. 3: 935-941.

Journal article
Published: 07 September 2016 in MM Science Journal
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ACS Style

Kamil Zidek; Jan Pitel; Alexander Hosovsky. DESIGN OF ADJUSTABLE SMART VISION SYSTEM BASEDON ARTIFICIAL MUSCLE ACTUATORS. MM Science Journal 2016, 2016, 947 -951.

AMA Style

Kamil Zidek, Jan Pitel, Alexander Hosovsky. DESIGN OF ADJUSTABLE SMART VISION SYSTEM BASEDON ARTIFICIAL MUSCLE ACTUATORS. MM Science Journal. 2016; 2016 (3):947-951.

Chicago/Turabian Style

Kamil Zidek; Jan Pitel; Alexander Hosovsky. 2016. "DESIGN OF ADJUSTABLE SMART VISION SYSTEM BASEDON ARTIFICIAL MUSCLE ACTUATORS." MM Science Journal 2016, no. 3: 947-951.

Journal article
Published: 01 September 2016 in Mechanism and Machine Theory
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Pneumatic artificial muscles (PAMs) belong to the group of nonconventional actuators with remarkable force/weight ratio that can be used for the construction of soft mechanisms safe in contact with humans. In order to be able to design an effective control of 2-link soft robot arm actuated with PAMs, a dynamic model of this system needs to be derived. We use a PAM dynamic model derived using first principles modeling (for contraction, pressure, and air flow dynamics) and ANFIS-based approximation based on the experimental data for the muscle force function. To derive the dynamics of the robot arm, we use Lagrangian mechanics approach for planar arm with the inertial and mass data based on the 3D CAD model. To validate the complete dynamic model of the soft robot arm, we used a gravity test (without PAM actuation) and pulse excitation for PAM control. The results confirm good validity of the dynamic model for all relevant variables (joint angles, muscle contractions, and pressures) as well as the dynamic coupling between the joints.

ACS Style

A. Hošovský; J. Piteľ; K. Židek; M. Tóthová; J. Sárosi; L. Cveticanin. Dynamic characterization and simulation of two-link soft robot arm with pneumatic muscles. Mechanism and Machine Theory 2016, 103, 98 -116.

AMA Style

A. Hošovský, J. Piteľ, K. Židek, M. Tóthová, J. Sárosi, L. Cveticanin. Dynamic characterization and simulation of two-link soft robot arm with pneumatic muscles. Mechanism and Machine Theory. 2016; 103 ():98-116.

Chicago/Turabian Style

A. Hošovský; J. Piteľ; K. Židek; M. Tóthová; J. Sárosi; L. Cveticanin. 2016. "Dynamic characterization and simulation of two-link soft robot arm with pneumatic muscles." Mechanism and Machine Theory 103, no. : 98-116.

Review
Published: 01 January 2016 in Procedia Engineering
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Abrasive water jet has been used for more than thirty years. It quickly became one of the core non-conventional technologies for cutting operations of various materials, ranging from soft and easy to cut materials, through ductile metal materials to brittle, hard to cut ceramics and metals. Abrasive water suspension jet (AWSJ) did not gain much popularity in machining industry in the beginning as opposed to abrasive water injection jet, but has found niche applications where its advantages were put to use. In this study a review of recent development in the eld of AWSJ is presented. Micro-machining, as a potential application for AWSJ, is discussed at the beginning, followed by several advances in the AWSJ technology. At the end, typical applications for AWSJ as decommissioning and dismantling of structures or rock drilling are presented.

ACS Style

Matúš Molitoris; Ján Piteľ; Alexander Hošovský; Mária Tóthová; Kamil Židek. A Review of Research on Water Jet with Slurry Injection. Procedia Engineering 2016, 149, 333 -339.

AMA Style

Matúš Molitoris, Ján Piteľ, Alexander Hošovský, Mária Tóthová, Kamil Židek. A Review of Research on Water Jet with Slurry Injection. Procedia Engineering. 2016; 149 ():333-339.

Chicago/Turabian Style

Matúš Molitoris; Ján Piteľ; Alexander Hošovský; Mária Tóthová; Kamil Židek. 2016. "A Review of Research on Water Jet with Slurry Injection." Procedia Engineering 149, no. : 333-339.