Pub Date : 2021-01-21DOI: 10.1109/SAMI50585.2021.9378635
K. Nakajima, V. Moshnyaga, Koji Hashimoto
This paper experimentally compares two face recognition approaches implemented on Raspberry-Pi in the smart-door system. The first approach is based on Local-Binary Patterns Histograms. The second one utilizes convolutional networks and deep learning. The paper describes the implementations and reports the results in terms of recognition accuracy and time. It shows that the CNN based approach runs faster and achieves better recognition accuracy than LBP even on small library sets and limited resources of Raspberry-Pi.
{"title":"A comparative study of conventional and CNN-based implementations of facial recognition on Raspberry-Pi","authors":"K. Nakajima, V. Moshnyaga, Koji Hashimoto","doi":"10.1109/SAMI50585.2021.9378635","DOIUrl":"https://doi.org/10.1109/SAMI50585.2021.9378635","url":null,"abstract":"This paper experimentally compares two face recognition approaches implemented on Raspberry-Pi in the smart-door system. The first approach is based on Local-Binary Patterns Histograms. The second one utilizes convolutional networks and deep learning. The paper describes the implementations and reports the results in terms of recognition accuracy and time. It shows that the CNN based approach runs faster and achieves better recognition accuracy than LBP even on small library sets and limited resources of Raspberry-Pi.","PeriodicalId":402414,"journal":{"name":"2021 IEEE 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133641840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-21DOI: 10.1109/SAMI50585.2021.9378665
J. Takács, Monika Pogátsnik
The COVID-19 epidemic has led to school closures worldwide. In Hungary, on 11 March 2020, the Government ordered a ban on university attendance, while on 13 March it also decided to switch to digital distance education in public education. Our research revolves around the educational challenge that emerged from the epidemic from a student perspective. We examined the provision of digital tools required for online education. We collected feedback from students on the pros and cons of online education. Access to technology is not enough for digital education, the change of pedagogical approach is also needed. We collected examples and suggestions for creative digital teaching practices, also based on the students' experience.
{"title":"The online learning from the students' perspective","authors":"J. Takács, Monika Pogátsnik","doi":"10.1109/SAMI50585.2021.9378665","DOIUrl":"https://doi.org/10.1109/SAMI50585.2021.9378665","url":null,"abstract":"The COVID-19 epidemic has led to school closures worldwide. In Hungary, on 11 March 2020, the Government ordered a ban on university attendance, while on 13 March it also decided to switch to digital distance education in public education. Our research revolves around the educational challenge that emerged from the epidemic from a student perspective. We examined the provision of digital tools required for online education. We collected feedback from students on the pros and cons of online education. Access to technology is not enough for digital education, the change of pedagogical approach is also needed. We collected examples and suggestions for creative digital teaching practices, also based on the students' experience.","PeriodicalId":402414,"journal":{"name":"2021 IEEE 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130413270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-21DOI: 10.1109/SAMI50585.2021.9378698
L. Horváth
It was a long way to engineering where model systems serve lifecycle engineering of systems organized achievements. Recently, contextual connections control integrated model objects including cyber units in engineering achievements (EAs). EA serves as collective concept for industrial and commercial products, experimental structures, prototypes in the engineering practice. Cyber units control physical units to provide autonomous features in EAs. Lifecycle engineering includes lifecycle innovation providing integrated research, development, and operation capabilities for EAs. This paper introduces some latest results in modeling methodology for EAs. It starts with introduction of a new general model of engineering serving the above scenario. The next structural unit of this paper analyses the new style of engineering through human behavior and communication. Following this, model centered style of integrated research, development, and operation activities are highlighted considering situation based decisions and physical activity control of EAs. Finally, implementation issues as providing cloud platform based software capabilities, realization of pure model centered communication are discussed as there are under establishing at the Virtual Research Laboratory (VRL) of the Doctoral School of applied Informatics and Applied Mathematics (DSAIAM, Óbuda University).
{"title":"Model Centered Engineering in Wide Context","authors":"L. Horváth","doi":"10.1109/SAMI50585.2021.9378698","DOIUrl":"https://doi.org/10.1109/SAMI50585.2021.9378698","url":null,"abstract":"It was a long way to engineering where model systems serve lifecycle engineering of systems organized achievements. Recently, contextual connections control integrated model objects including cyber units in engineering achievements (EAs). EA serves as collective concept for industrial and commercial products, experimental structures, prototypes in the engineering practice. Cyber units control physical units to provide autonomous features in EAs. Lifecycle engineering includes lifecycle innovation providing integrated research, development, and operation capabilities for EAs. This paper introduces some latest results in modeling methodology for EAs. It starts with introduction of a new general model of engineering serving the above scenario. The next structural unit of this paper analyses the new style of engineering through human behavior and communication. Following this, model centered style of integrated research, development, and operation activities are highlighted considering situation based decisions and physical activity control of EAs. Finally, implementation issues as providing cloud platform based software capabilities, realization of pure model centered communication are discussed as there are under establishing at the Virtual Research Laboratory (VRL) of the Doctoral School of applied Informatics and Applied Mathematics (DSAIAM, Óbuda University).","PeriodicalId":402414,"journal":{"name":"2021 IEEE 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI)","volume":"409 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134236689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-21DOI: 10.1109/SAMI50585.2021.9378630
Martin Stancel, B. Madoš, M. Chovanec, P. Baláž
This paper describes a combination of color determination and object detection. It describes the creation of a hybrid system that would increase production and streamline the process of crop harvesting. The system aims to delineate all potential crops by determining color. If the potential crops are of the sufficient size then object detection is performed using YOLO technology which determines the confidence of strawberry prediction. The main part is the analysis and the implementation of this hybrid system in Python. The last part of the paper is devoted to the evaluation and verification of the created system.
{"title":"Hybrid Object Detection Using Domain-Specific Datasets","authors":"Martin Stancel, B. Madoš, M. Chovanec, P. Baláž","doi":"10.1109/SAMI50585.2021.9378630","DOIUrl":"https://doi.org/10.1109/SAMI50585.2021.9378630","url":null,"abstract":"This paper describes a combination of color determination and object detection. It describes the creation of a hybrid system that would increase production and streamline the process of crop harvesting. The system aims to delineate all potential crops by determining color. If the potential crops are of the sufficient size then object detection is performed using YOLO technology which determines the confidence of strawberry prediction. The main part is the analysis and the implementation of this hybrid system in Python. The last part of the paper is devoted to the evaluation and verification of the created system.","PeriodicalId":402414,"journal":{"name":"2021 IEEE 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128961475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-21DOI: 10.1109/SAMI50585.2021.9378683
Ashit Gupta, V. Masampally, Vishal Jadhav, A. Deodhar, V. Runkana
Changes in operating conditions, environment, and deterioration of structural health of components over time leads to unplanned outages in industrial equipment. A multicomponent industrial system may fail when one or more of its components deteriorate beyond a certain limit. The deterioration is often a gradual and continuous process, culminating in sudden failure of an equipment. However, the components in a system may show some early signs of deterioration that might not be explicitly apparent even to domain experts. Therefore, advanced algorithms are required for early detection of these signatures of failure to enable corrective actions in time. A set of algorithms is presented here to detect signatures of failure from the continuous sensor data in a multicomponent system. Each system consists of four identical components, each with a different timing of failure. A set of Long Short-Term Memory (LSTM) based algorithms are employed to identify the onset of abnormal behavior. An ensemble framework, which minimizes the frequency of false and missed alarms is proposed and its performance is compared with other stand-alone algorithms. An ensemble approach on top of a set of LSTM-based models performed better than the individual algorithms.
{"title":"Supervised Operational Change Point Detection using Ensemble Long-Short Term Memory in a Multicomponent Industrial System","authors":"Ashit Gupta, V. Masampally, Vishal Jadhav, A. Deodhar, V. Runkana","doi":"10.1109/SAMI50585.2021.9378683","DOIUrl":"https://doi.org/10.1109/SAMI50585.2021.9378683","url":null,"abstract":"Changes in operating conditions, environment, and deterioration of structural health of components over time leads to unplanned outages in industrial equipment. A multicomponent industrial system may fail when one or more of its components deteriorate beyond a certain limit. The deterioration is often a gradual and continuous process, culminating in sudden failure of an equipment. However, the components in a system may show some early signs of deterioration that might not be explicitly apparent even to domain experts. Therefore, advanced algorithms are required for early detection of these signatures of failure to enable corrective actions in time. A set of algorithms is presented here to detect signatures of failure from the continuous sensor data in a multicomponent system. Each system consists of four identical components, each with a different timing of failure. A set of Long Short-Term Memory (LSTM) based algorithms are employed to identify the onset of abnormal behavior. An ensemble framework, which minimizes the frequency of false and missed alarms is proposed and its performance is compared with other stand-alone algorithms. An ensemble approach on top of a set of LSTM-based models performed better than the individual algorithms.","PeriodicalId":402414,"journal":{"name":"2021 IEEE 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114855526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-21DOI: 10.1109/SAMI50585.2021.9378659
Norbert Somogyi, Gábor Kövesdán
As software engineering techniques and practices continuously evolve, programs created with an older technology stack become harder and more costly to maintain. These software are often referred to as legacy code. Naturally, the need arises to make use of the newer and more effective technologies, making the legacy code easier to maintain and operate. However, companies rarely allocate the necessary resources to manually re-implement these systems as that would be highly time-consuming and extremely costly to spend exclusively for maintenance purposes. Thus, various code modernization approaches have been proposed and tools have been created to reduce the cost of re-implementation by semi-automatically translating legacy systems into a modern, more advantageous environment. However, the source and target languages may be so different in nature that making the generated code feel as natural as possible is often difficult. These linguistic differences frequently impose the emulation of certain features between the two languages, which may prove too difficult to automatically handle using conventional static analysis of the source code. To this end, in this paper we propose the novel method of using machine learning techniques to teach the transformer on how to effectively handle cases that would otherwise be very error-prone in practice. This way, the transformation tool can achieve both a high level of automation and the ability to generate precise, error free code.
{"title":"Software Modernization Using Machine Learning Techniques","authors":"Norbert Somogyi, Gábor Kövesdán","doi":"10.1109/SAMI50585.2021.9378659","DOIUrl":"https://doi.org/10.1109/SAMI50585.2021.9378659","url":null,"abstract":"As software engineering techniques and practices continuously evolve, programs created with an older technology stack become harder and more costly to maintain. These software are often referred to as legacy code. Naturally, the need arises to make use of the newer and more effective technologies, making the legacy code easier to maintain and operate. However, companies rarely allocate the necessary resources to manually re-implement these systems as that would be highly time-consuming and extremely costly to spend exclusively for maintenance purposes. Thus, various code modernization approaches have been proposed and tools have been created to reduce the cost of re-implementation by semi-automatically translating legacy systems into a modern, more advantageous environment. However, the source and target languages may be so different in nature that making the generated code feel as natural as possible is often difficult. These linguistic differences frequently impose the emulation of certain features between the two languages, which may prove too difficult to automatically handle using conventional static analysis of the source code. To this end, in this paper we propose the novel method of using machine learning techniques to teach the transformer on how to effectively handle cases that would otherwise be very error-prone in practice. This way, the transformation tool can achieve both a high level of automation and the ability to generate precise, error free code.","PeriodicalId":402414,"journal":{"name":"2021 IEEE 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126538909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-21DOI: 10.1109/SAMI50585.2021.9378650
Klaudia Ivancová, M. Sarnovský, Viera Maslej-Krcšñáková
In recent years, the spreading of fake news presents a serious issue in the online environment. Automatic methods able to identify them from the text are being massively explored and deployed on social platforms and online media. Such detection methods are based on a combination of natural language processing and machine learning techniques. Deep learning became a very popular choice in many text processing tasks, fake news detection included. Numerous studies apply the advanced deep learning models to detect fake news and related phenomena from the English text. This paper focuses on the detection of fake news from the news articles written in the Slovak language. To successfully train deep learning models, we created a labelled dataset consisting of the political news articles published by online news portals as well as suspicious conspiratory portals. We trained two architectures, CNN and LSTM neural networks using this data. The performance of the models was experimentally evaluated using standard classification metrics.
{"title":"Fake news detection in Slovak language using deep learning techniques","authors":"Klaudia Ivancová, M. Sarnovský, Viera Maslej-Krcšñáková","doi":"10.1109/SAMI50585.2021.9378650","DOIUrl":"https://doi.org/10.1109/SAMI50585.2021.9378650","url":null,"abstract":"In recent years, the spreading of fake news presents a serious issue in the online environment. Automatic methods able to identify them from the text are being massively explored and deployed on social platforms and online media. Such detection methods are based on a combination of natural language processing and machine learning techniques. Deep learning became a very popular choice in many text processing tasks, fake news detection included. Numerous studies apply the advanced deep learning models to detect fake news and related phenomena from the English text. This paper focuses on the detection of fake news from the news articles written in the Slovak language. To successfully train deep learning models, we created a labelled dataset consisting of the political news articles published by online news portals as well as suspicious conspiratory portals. We trained two architectures, CNN and LSTM neural networks using this data. The performance of the models was experimentally evaluated using standard classification metrics.","PeriodicalId":402414,"journal":{"name":"2021 IEEE 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124411579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-21DOI: 10.1109/SAMI50585.2021.9378661
T. Bálint, A. Balogová, R. Hudák, J. Živčák, M. Schnitzer, J. Feranc
In order to carry out mechanical testing of samples printed by using additive technology, it is necessary to specify the parameters of the production of filaments, the parameters of 3D printing and the parameters of mechanical testing. In this article, I will discuss the production of filaments, additive technology for printing samples from PLA/PHB material used for detailed mechanical tests and subsequently for evaluation of these mechanical tests. The real-world application of PLA/PHB products bring great benefits. The aim of this paper is to perform mechanical tests on extruded PLA/PHB samples with three different TAC solvent concentrations. Samples were printed using additive technology. The comparison of the results of the pressure and tensile testing carried out on the apparatus also contributed to the success of the research.
{"title":"Production, additive printing and mechanical testing of PLA/PHB material with different concentrations of TAC emollient","authors":"T. Bálint, A. Balogová, R. Hudák, J. Živčák, M. Schnitzer, J. Feranc","doi":"10.1109/SAMI50585.2021.9378661","DOIUrl":"https://doi.org/10.1109/SAMI50585.2021.9378661","url":null,"abstract":"In order to carry out mechanical testing of samples printed by using additive technology, it is necessary to specify the parameters of the production of filaments, the parameters of 3D printing and the parameters of mechanical testing. In this article, I will discuss the production of filaments, additive technology for printing samples from PLA/PHB material used for detailed mechanical tests and subsequently for evaluation of these mechanical tests. The real-world application of PLA/PHB products bring great benefits. The aim of this paper is to perform mechanical tests on extruded PLA/PHB samples with three different TAC solvent concentrations. Samples were printed using additive technology. The comparison of the results of the pressure and tensile testing carried out on the apparatus also contributed to the success of the research.","PeriodicalId":402414,"journal":{"name":"2021 IEEE 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123648709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-21DOI: 10.1109/SAMI50585.2021.9378660
Taehee Jeong, Kunj J. Parikh, Raymond Chau, C. Huang, H. Chan, Hyeran Jeon
Cluster tool is a core manufacturing system in semiconductor industry. Optimizing the schedule of operations of a cluster tool is important because it is directly connected with its productivity. The scheduling becomes more complicated as the number of operating steps increases. There have been extensive studies to model the cluster tool operations and predict its throughput for a given configuration. However, the theoretical models cannot reflect realtime issues and the state-of-the-art throughput models are hard to be applied to predict scheduling parameters. In this work, we characterize the unique behavioral pattern of a key scheduling parameter towards the cluster tool throughput, and propose a novel deep-learning model that effectively identifies the best scheduling parameters. A two-stage model is designed that consists of an one-dimensional convolution neural network and a semantic segmentation network. Our experimental results show that the proposed model shows a superial accuracy than the state-of-the-art DNN solution for the best scheduling parameter detection.
{"title":"Two-Stage Sequence Model for Maximum Throughput in Cluster Tools","authors":"Taehee Jeong, Kunj J. Parikh, Raymond Chau, C. Huang, H. Chan, Hyeran Jeon","doi":"10.1109/SAMI50585.2021.9378660","DOIUrl":"https://doi.org/10.1109/SAMI50585.2021.9378660","url":null,"abstract":"Cluster tool is a core manufacturing system in semiconductor industry. Optimizing the schedule of operations of a cluster tool is important because it is directly connected with its productivity. The scheduling becomes more complicated as the number of operating steps increases. There have been extensive studies to model the cluster tool operations and predict its throughput for a given configuration. However, the theoretical models cannot reflect realtime issues and the state-of-the-art throughput models are hard to be applied to predict scheduling parameters. In this work, we characterize the unique behavioral pattern of a key scheduling parameter towards the cluster tool throughput, and propose a novel deep-learning model that effectively identifies the best scheduling parameters. A two-stage model is designed that consists of an one-dimensional convolution neural network and a semantic segmentation network. Our experimental results show that the proposed model shows a superial accuracy than the state-of-the-art DNN solution for the best scheduling parameter detection.","PeriodicalId":402414,"journal":{"name":"2021 IEEE 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126288293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-21DOI: 10.1109/SAMI50585.2021.9378620
J. Živčák, M. Kelemen, Ivan Virgala, Peter Marcinko, P. Tuleja, Marek Sukop, E. Prada, Martin Varga, J. Ligus, Filip Filakovský
The paper deals with development of an artificial lung ventilation. The aim of the paper is to present developed ventilator based on bag-valve-mask, which could be used as alternative to mechanical ventilator in critical situations related to COVID-19. At first, we present basic principles of positive pressure ventilation. Subsequently, we introduce a requirements to emergency mechanical ventilator in order to be suitable alternative in hospitals as well as in households. The mechanical and control design are presented in the next section. Finally, we experimentally verify developed ventilator with focus on measured pressure of patient airways. The presented results show a potential of developed ventilator to be used at practical level.
{"title":"A Portable BVM-based Emergency Mechanical Ventilator","authors":"J. Živčák, M. Kelemen, Ivan Virgala, Peter Marcinko, P. Tuleja, Marek Sukop, E. Prada, Martin Varga, J. Ligus, Filip Filakovský","doi":"10.1109/SAMI50585.2021.9378620","DOIUrl":"https://doi.org/10.1109/SAMI50585.2021.9378620","url":null,"abstract":"The paper deals with development of an artificial lung ventilation. The aim of the paper is to present developed ventilator based on bag-valve-mask, which could be used as alternative to mechanical ventilator in critical situations related to COVID-19. At first, we present basic principles of positive pressure ventilation. Subsequently, we introduce a requirements to emergency mechanical ventilator in order to be suitable alternative in hospitals as well as in households. The mechanical and control design are presented in the next section. Finally, we experimentally verify developed ventilator with focus on measured pressure of patient airways. The presented results show a potential of developed ventilator to be used at practical level.","PeriodicalId":402414,"journal":{"name":"2021 IEEE 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125452086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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