Pub Date : 2021-01-21DOI: 10.1109/SAMI50585.2021.9378653
Béla Surányi, L. Kovács, L. Szilágyi
The automatic segmentation of medical images is an intensely investigated problem, due to the quick rise of medical image data amount created day by day, which cannot be followed by the number of human experts. This paper searches for the most suitable classical machine learning method to be employed in the segmentation of various tissue types from volumetric multi-spectral MRI records of 6-month infant patients. Model training and model based prediction is performed using the 10 records of the train data set available at the iSeg-2017 challenge. All MRI records are treated with histogram normalization and feature generation, and then fed to six machine learning methods, which use them as train and test data according to the leave-one-out technique. The output of the classification algorithms is evaluated with statistical methods. The best segmentation accuracy is achieved by the random forest based approach, with a correct decision rate of 83.4%.
{"title":"Segmentation of Brain Tissues from Infant MRI Records Using Machine Learning Techniques","authors":"Béla Surányi, L. Kovács, L. Szilágyi","doi":"10.1109/SAMI50585.2021.9378653","DOIUrl":"https://doi.org/10.1109/SAMI50585.2021.9378653","url":null,"abstract":"The automatic segmentation of medical images is an intensely investigated problem, due to the quick rise of medical image data amount created day by day, which cannot be followed by the number of human experts. This paper searches for the most suitable classical machine learning method to be employed in the segmentation of various tissue types from volumetric multi-spectral MRI records of 6-month infant patients. Model training and model based prediction is performed using the 10 records of the train data set available at the iSeg-2017 challenge. All MRI records are treated with histogram normalization and feature generation, and then fed to six machine learning methods, which use them as train and test data according to the leave-one-out technique. The output of the classification algorithms is evaluated with statistical methods. The best segmentation accuracy is achieved by the random forest based approach, with a correct decision rate of 83.4%.","PeriodicalId":402414,"journal":{"name":"2021 IEEE 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI)","volume":"233 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":"127530032","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/SAMI50585.2021.9378664
Ernő Horváth, C. Pozna
Lately self-driving navigation and control have obtained significant attention in many fields, such as mobile robotics or autonomous driving. Although sensing, perception, planning and following subtasks associated with autonomous vehicles persist with open challenges. In this paper the autonomous following subtask is targeted. The paper proposes trajectory following approach which is designed for self-driving vehicles.
{"title":"Clothoid-based Trajectory Following Approach for Self-driving vehicles","authors":"Ernő Horváth, C. Pozna","doi":"10.1109/SAMI50585.2021.9378664","DOIUrl":"https://doi.org/10.1109/SAMI50585.2021.9378664","url":null,"abstract":"Lately self-driving navigation and control have obtained significant attention in many fields, such as mobile robotics or autonomous driving. Although sensing, perception, planning and following subtasks associated with autonomous vehicles persist with open challenges. In this paper the autonomous following subtask is targeted. The paper proposes trajectory following approach which is designed for self-driving vehicles.","PeriodicalId":402414,"journal":{"name":"2021 IEEE 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI)","volume":"35 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":"121853079","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/SAMI50585.2021.9378657
Kaziwa Saleh, S. Szénási, Z. Vámossy
The significant power of deep learning networks has led to enormous development in object detection. Over the last few years, object detector frameworks have achieved tremendous success in both accuracy and efficiency. However, their ability is far from that of human beings due to several factors, occlusion being one of them. Since occlusion can happen in various locations, scale, and ratio, it is very difficult to handle. In this paper, we address the challenges in occlusion handling in generic object detection in both outdoor and indoor scenes, then we refer to the recent works that have been carried out to overcome these challenges. Finally, we discuss some possible future directions of research.
{"title":"Occlusion Handling in Generic Object Detection: A Review","authors":"Kaziwa Saleh, S. Szénási, Z. Vámossy","doi":"10.1109/SAMI50585.2021.9378657","DOIUrl":"https://doi.org/10.1109/SAMI50585.2021.9378657","url":null,"abstract":"The significant power of deep learning networks has led to enormous development in object detection. Over the last few years, object detector frameworks have achieved tremendous success in both accuracy and efficiency. However, their ability is far from that of human beings due to several factors, occlusion being one of them. Since occlusion can happen in various locations, scale, and ratio, it is very difficult to handle. In this paper, we address the challenges in occlusion handling in generic object detection in both outdoor and indoor scenes, then we refer to the recent works that have been carried out to overcome these challenges. Finally, we discuss some possible future directions of research.","PeriodicalId":402414,"journal":{"name":"2021 IEEE 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI)","volume":"29 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":"129061839","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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