Pub Date : 2019-09-01DOI: 10.1109/idaacs.2019.8924256
Yevhen Vasiliu, I. Limar, T. Gancarczyk, M. Karpinski
A new quantum secret sharing protocol based on the ping-pong protocol of quantum secure direct communication is proposed. The pairs of entangled qutrits are used in protocol, which allows an increase in the information capacity compared with protocols based on entangled qubits. The detection of channel eavesdropping used in the protocol is being implemented in random moments of time, thereby it is possible do not use the significant amount of quantum memory. The security of the proposed protocol to attacks is considered. A method for additional amplification of the security to an eavesdropping attack in communication channels for the developed protocol is proposed.
{"title":"New Quantum Secret Sharing Protocol Using Entangled Qutrits","authors":"Yevhen Vasiliu, I. Limar, T. Gancarczyk, M. Karpinski","doi":"10.1109/idaacs.2019.8924256","DOIUrl":"https://doi.org/10.1109/idaacs.2019.8924256","url":null,"abstract":"A new quantum secret sharing protocol based on the ping-pong protocol of quantum secure direct communication is proposed. The pairs of entangled qutrits are used in protocol, which allows an increase in the information capacity compared with protocols based on entangled qubits. The detection of channel eavesdropping used in the protocol is being implemented in random moments of time, thereby it is possible do not use the significant amount of quantum memory. The security of the proposed protocol to attacks is considered. A method for additional amplification of the security to an eavesdropping attack in communication channels for the developed protocol is proposed.","PeriodicalId":415006,"journal":{"name":"2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124393041","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 : 2019-09-01DOI: 10.1109/IDAACS.2019.8924461
V. Krylov, A. Sachenko, Pavlo Strubytskyi, Dmytro Lendiuk, H. Lipyanina, D. Zahorodnia, Vitaliy Dorosh, T. Lendyuk
The object of the study is the automation process for tourist demand modeling, the characteristic feature of which is consideration of the most important factors. Demand is one of these factors, which stimulates the development of tourism. Information technology for tourist demand modeling with characteristics consideration of the most important factors is developed using the programming language R and a package Shiny.
{"title":"Multiple Regression Method for Analyzing the Tourist Demand Considering the Influence Factors","authors":"V. Krylov, A. Sachenko, Pavlo Strubytskyi, Dmytro Lendiuk, H. Lipyanina, D. Zahorodnia, Vitaliy Dorosh, T. Lendyuk","doi":"10.1109/IDAACS.2019.8924461","DOIUrl":"https://doi.org/10.1109/IDAACS.2019.8924461","url":null,"abstract":"The object of the study is the automation process for tourist demand modeling, the characteristic feature of which is consideration of the most important factors. Demand is one of these factors, which stimulates the development of tourism. Information technology for tourist demand modeling with characteristics consideration of the most important factors is developed using the programming language R and a package Shiny.","PeriodicalId":415006,"journal":{"name":"2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117156795","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 : 2019-09-01DOI: 10.1109/IDAACS.2019.8924354
I. Kandyba, Yevhen Davydenko, V. Panasyuk, A. Shved, M. Fisun
Recently, domain-specific language has become more popular in the IT market. Therefore, there are several variants of the domain-oriented programming languages used in the modern world. A variant of domain-specific language that accepts wind farm modeling language at the input and GPSS code at the output to model the queuing system has been proposed in this paper. The ANTLR grammar rules for the implementation of wind farm modeling language have been formed.
{"title":"ANTRL as a Development Platform for the Series DSL for the Learning Process","authors":"I. Kandyba, Yevhen Davydenko, V. Panasyuk, A. Shved, M. Fisun","doi":"10.1109/IDAACS.2019.8924354","DOIUrl":"https://doi.org/10.1109/IDAACS.2019.8924354","url":null,"abstract":"Recently, domain-specific language has become more popular in the IT market. Therefore, there are several variants of the domain-oriented programming languages used in the modern world. A variant of domain-specific language that accepts wind farm modeling language at the input and GPSS code at the output to model the queuing system has been proposed in this paper. The ANTLR grammar rules for the implementation of wind farm modeling language have been formed.","PeriodicalId":415006,"journal":{"name":"2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS)","volume":"24 5-6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123567354","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 : 2019-09-01DOI: 10.1109/idaacs.2019.8924257
It’s our pleasure to welcome all attendees the 2019 IEEE 10 International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), www.idaacs.net, which will be held in Metz, France, 18-21 September, 2019. The main goal of IDAACS’2019 is to provide a forum for high-quality reports on the state-ofthe-art Theory, Technology and Applications of Intelligent Data Acquisition and Advanced Computer Systems as used in different areas. A family of IDAACS Workshops has already been created since the IEEE 1st IDAACS Workshop was held in Foros, Crimea, Ukraine, July 1-4, 2001. After that the following IDAACS Conferences were held in Lviv, Ukraine, 2003, Sofia, Bulgaria, 2005, Dortmund, Germany, 2007, Rende (Cosenza), Italy, 2009, Prague, Czech Republic, 2011, Berlin, Germany, 2013, Warsaw, Poland, 2015, Bucharest, Romania, 2017. Moreover, IDAACS Symposia on Wireless Systems (SWS) were held in Offenburg, Germany in 2012, 2014, 2016 as well as in Lviv, Ukraine in 2018. The IDAACS 2019 Conference is organized by IEEE Ukraine Section I&M / CI Joint Societies Chapter and the Research Institute for Intelligent Computer Systems, Ternopil National Economic University (TNEU) and V.M. Glushkov Institute of Cybernetics, National Academy of Sciences, Ukraine in cooperation with the ENIM (Ecole Nationale d’Ingénieurs de Metz) and LCOMS (Laboratory of Conception, Optimisation and Modelling of Systems) of University of Lorraine, France. It is supported and sponsored by IEEE Ukraine Section, IEEE France Section, MDPI Sensors, Fondation ENIM, Metz Metropole, River Publishers, so we express our sincere gratitude to each of them. The International Program Committee of IDAACS’2019 is co-chaired by Francesca Guerriero, Italy and Carsten Wolff, Germany, many thanks to both of them. In addition, we express our gratitude for all members of IPC. There were submitted the 314 papers by authors from the 42 countries. Many thanks to all the reviewers, their names are listed in the proceedings and their contribution to the quality and success of this Conference. After the reviewing process, the 213 papers were accepted for a publication in the Conference proceedings. All the presentations are organized into the 27 oral and the 3 poster sessions. In addition, three prominent experts are invited to deliver keynotes during plenary sessions: Jürgen Sieck, University of Applied Sciences, Berlin, Germany; Kurosh Madani, Paris-Est Créteil Val-de-Marne University, France; and Fabio Scotti, University of Milan, Italy. We appreciate their contribution to the IDAACS 2019 Conference very much. Besides, the IDAACS 2019 remained its peculiarity providing seven special streams, and Workshops on Cyber Physical Systems and Internet of Things with 11 sessions. Metz is the economic heart of the Lorraine region, it’s home to the University of Lorraine. The university has over 60000 students, close to 6900 staff members, among which 3700 faculty and sea
{"title":"Message from the IDAACS 2019 Co-Chairmen","authors":"","doi":"10.1109/idaacs.2019.8924257","DOIUrl":"https://doi.org/10.1109/idaacs.2019.8924257","url":null,"abstract":"It’s our pleasure to welcome all attendees the 2019 IEEE 10 International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), www.idaacs.net, which will be held in Metz, France, 18-21 September, 2019. The main goal of IDAACS’2019 is to provide a forum for high-quality reports on the state-ofthe-art Theory, Technology and Applications of Intelligent Data Acquisition and Advanced Computer Systems as used in different areas. A family of IDAACS Workshops has already been created since the IEEE 1st IDAACS Workshop was held in Foros, Crimea, Ukraine, July 1-4, 2001. After that the following IDAACS Conferences were held in Lviv, Ukraine, 2003, Sofia, Bulgaria, 2005, Dortmund, Germany, 2007, Rende (Cosenza), Italy, 2009, Prague, Czech Republic, 2011, Berlin, Germany, 2013, Warsaw, Poland, 2015, Bucharest, Romania, 2017. Moreover, IDAACS Symposia on Wireless Systems (SWS) were held in Offenburg, Germany in 2012, 2014, 2016 as well as in Lviv, Ukraine in 2018. The IDAACS 2019 Conference is organized by IEEE Ukraine Section I&M / CI Joint Societies Chapter and the Research Institute for Intelligent Computer Systems, Ternopil National Economic University (TNEU) and V.M. Glushkov Institute of Cybernetics, National Academy of Sciences, Ukraine in cooperation with the ENIM (Ecole Nationale d’Ingénieurs de Metz) and LCOMS (Laboratory of Conception, Optimisation and Modelling of Systems) of University of Lorraine, France. It is supported and sponsored by IEEE Ukraine Section, IEEE France Section, MDPI Sensors, Fondation ENIM, Metz Metropole, River Publishers, so we express our sincere gratitude to each of them. The International Program Committee of IDAACS’2019 is co-chaired by Francesca Guerriero, Italy and Carsten Wolff, Germany, many thanks to both of them. In addition, we express our gratitude for all members of IPC. There were submitted the 314 papers by authors from the 42 countries. Many thanks to all the reviewers, their names are listed in the proceedings and their contribution to the quality and success of this Conference. After the reviewing process, the 213 papers were accepted for a publication in the Conference proceedings. All the presentations are organized into the 27 oral and the 3 poster sessions. In addition, three prominent experts are invited to deliver keynotes during plenary sessions: Jürgen Sieck, University of Applied Sciences, Berlin, Germany; Kurosh Madani, Paris-Est Créteil Val-de-Marne University, France; and Fabio Scotti, University of Milan, Italy. We appreciate their contribution to the IDAACS 2019 Conference very much. Besides, the IDAACS 2019 remained its peculiarity providing seven special streams, and Workshops on Cyber Physical Systems and Internet of Things with 11 sessions. Metz is the economic heart of the Lorraine region, it’s home to the University of Lorraine. The university has over 60000 students, close to 6900 staff members, among which 3700 faculty and sea","PeriodicalId":415006,"journal":{"name":"2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126808566","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 : 2019-09-01DOI: 10.1109/IDAACS.2019.8924345
V. Martsenyuk, L. Babinets, Y. Dronyak, Olha Paslay, O. Veselska, K. Warwas, I. Andrushchak, A. Kłos-Witkowska
The purpose of the work is to develop mathematical and software background for the development of machine learning (ML) models in differential diagnostics of comorbid states. Flowchart includes basic steps of ML model development, including the statement of task, the choice of method (learner), setting its parameters and model assessment. The problems dealing with dimension reduction which arise often in differential diagnostics of comorbid states are highlighted and solved with help of modified PCA method. As an example we consider the problem of development of classifier for chronic pancreatitis combined with ascaridosis where we solve all tasks of ML model development. With help of benchmark of learners in the package mlr we compare different methods of ML when applying them in differential diagnostics of comorbid states.
{"title":"On Development of Machine Learning Models with Aim of Medical Differential Diagnostics of the Comorbid States","authors":"V. Martsenyuk, L. Babinets, Y. Dronyak, Olha Paslay, O. Veselska, K. Warwas, I. Andrushchak, A. Kłos-Witkowska","doi":"10.1109/IDAACS.2019.8924345","DOIUrl":"https://doi.org/10.1109/IDAACS.2019.8924345","url":null,"abstract":"The purpose of the work is to develop mathematical and software background for the development of machine learning (ML) models in differential diagnostics of comorbid states. Flowchart includes basic steps of ML model development, including the statement of task, the choice of method (learner), setting its parameters and model assessment. The problems dealing with dimension reduction which arise often in differential diagnostics of comorbid states are highlighted and solved with help of modified PCA method. As an example we consider the problem of development of classifier for chronic pancreatitis combined with ascaridosis where we solve all tasks of ML model development. With help of benchmark of learners in the package mlr we compare different methods of ML when applying them in differential diagnostics of comorbid states.","PeriodicalId":415006,"journal":{"name":"2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114143793","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 : 2019-09-01DOI: 10.1109/IDAACS.2019.8924338
S. Antoshchuk, Olena Arsirii, O. Blazhko, Yuliia Troianovska, Tetiana Luhova
The article presents a method for detecting errors in the design of virtual environments, which involves the use of UML diagrams, and the mathematical apparatus of Petri Nets. The analysis of the features of the use of UML diagrams in the design of mechanics of virtual environments, as well as Petri Nets for their static analysis and dynamic modeling have been carried out. The work of this method is demonstrated on the example of the design of the mechanics of the game “Snake”.
{"title":"Method for Detecting Error in Design of Virtual Environment","authors":"S. Antoshchuk, Olena Arsirii, O. Blazhko, Yuliia Troianovska, Tetiana Luhova","doi":"10.1109/IDAACS.2019.8924338","DOIUrl":"https://doi.org/10.1109/IDAACS.2019.8924338","url":null,"abstract":"The article presents a method for detecting errors in the design of virtual environments, which involves the use of UML diagrams, and the mathematical apparatus of Petri Nets. The analysis of the features of the use of UML diagrams in the design of mechanics of virtual environments, as well as Petri Nets for their static analysis and dynamic modeling have been carried out. The work of this method is demonstrated on the example of the design of the mechanics of the game “Snake”.","PeriodicalId":415006,"journal":{"name":"2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116757883","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 : 2019-09-01DOI: 10.1109/IDAACS.2019.8924348
Eslam M. Mustafa, M. Fouad, M. Elshafey
Blind image steganalysis is defined as the binary classification problem of predicting whether or not an image contains an embedded message. With the development of steganography, extracting powerful features from the stego-images becomes a challenge. Recently, convolutional Neural Networks (CNNs) are presented as a promising solution for such a challenge. Unlike traditional steganalysis approaches, CNN-based steganalysis approaches have the ability of extracting features automatically from input images. With such an ability, there is no need to handcraft feature extractors like those used by traditional steganalysis approaches. Despite its long clinical success, CNN-based steganalysis approaches are time consuming. Training on those approaches may stand for days and sometimes for weeks. It is necessary to accelerate the training on CNN-based approaches to make them more usable in practice, especially for some real-time applications. The purpose of this paper is to implement an enhanced version of the improved Gaussian-Neuron CNN (IGNCNN) steganalysis approach on GPUs, and to profiteer the parallel power of GPUS. In this paper two approaches for parallelizing the CNN training process are proposed. The first is to apply the concept of data parallelism with the feature extraction module and the second is to apply model parallelism with the classification module. Besides the parallelization approaches, a variable batch size is implemented as an optimization approach. Using a big batch size in fully-connected layers leads to faster convergence to a better minima, but it may negatively affect the accuracy. The results of the proposed approach show that it outperforms the IGNCNN in terms of accuracy and performance metrics.
{"title":"Enhancing the Performance of an Image Steganalysis Approach Using Variable Batch Size-Based CNN on GPUs","authors":"Eslam M. Mustafa, M. Fouad, M. Elshafey","doi":"10.1109/IDAACS.2019.8924348","DOIUrl":"https://doi.org/10.1109/IDAACS.2019.8924348","url":null,"abstract":"Blind image steganalysis is defined as the binary classification problem of predicting whether or not an image contains an embedded message. With the development of steganography, extracting powerful features from the stego-images becomes a challenge. Recently, convolutional Neural Networks (CNNs) are presented as a promising solution for such a challenge. Unlike traditional steganalysis approaches, CNN-based steganalysis approaches have the ability of extracting features automatically from input images. With such an ability, there is no need to handcraft feature extractors like those used by traditional steganalysis approaches. Despite its long clinical success, CNN-based steganalysis approaches are time consuming. Training on those approaches may stand for days and sometimes for weeks. It is necessary to accelerate the training on CNN-based approaches to make them more usable in practice, especially for some real-time applications. The purpose of this paper is to implement an enhanced version of the improved Gaussian-Neuron CNN (IGNCNN) steganalysis approach on GPUs, and to profiteer the parallel power of GPUS. In this paper two approaches for parallelizing the CNN training process are proposed. The first is to apply the concept of data parallelism with the feature extraction module and the second is to apply model parallelism with the classification module. Besides the parallelization approaches, a variable batch size is implemented as an optimization approach. Using a big batch size in fully-connected layers leads to faster convergence to a better minima, but it may negatively affect the accuracy. The results of the proposed approach show that it outperforms the IGNCNN in terms of accuracy and performance metrics.","PeriodicalId":415006,"journal":{"name":"2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129664182","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 : 2019-09-01DOI: 10.1109/IDAACS.2019.8924458
Ahmed Yahia Kallel, Sebastian Uziel, Manuel Schappacher, A. Sikora, T. Keutel, O. Kanoun
The monitoring of industrial environments ensures that highly automated processes run without interruption. However, even if the industrial machines themselves are monitored, the communication lines are currently not continuously monitored in todays installations. They are checked usually only during maintenance intervals or in case of error. In addition, the cables or connected machines usually have to be removed from the system for the duration of the test. To overcome these drawbacks, we have developed and implemented a cost-efficient and continuous signal monitoring of Ethernet-based industrial bus systems. Several methods have been developed to assess the quality of the cable. These methods can be classified to either passive or active. Active methods are not suitable if interruption of the communication is undesired. Passive methods, on the other hand, require oversampling, which calls for expensive hardware. In this paper, a novel passive method combined with undersampling targeting cost-efficient hardware is proposed.
{"title":"A Cost-Efficient and Continuous Ethernet Cable Diagnosis Technique based on Undersampling","authors":"Ahmed Yahia Kallel, Sebastian Uziel, Manuel Schappacher, A. Sikora, T. Keutel, O. Kanoun","doi":"10.1109/IDAACS.2019.8924458","DOIUrl":"https://doi.org/10.1109/IDAACS.2019.8924458","url":null,"abstract":"The monitoring of industrial environments ensures that highly automated processes run without interruption. However, even if the industrial machines themselves are monitored, the communication lines are currently not continuously monitored in todays installations. They are checked usually only during maintenance intervals or in case of error. In addition, the cables or connected machines usually have to be removed from the system for the duration of the test. To overcome these drawbacks, we have developed and implemented a cost-efficient and continuous signal monitoring of Ethernet-based industrial bus systems. Several methods have been developed to assess the quality of the cable. These methods can be classified to either passive or active. Active methods are not suitable if interruption of the communication is undesired. Passive methods, on the other hand, require oversampling, which calls for expensive hardware. In this paper, a novel passive method combined with undersampling targeting cost-efficient hardware is proposed.","PeriodicalId":415006,"journal":{"name":"2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130314022","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 : 2019-09-01DOI: 10.1109/idaacs.2019.8924314
Oleksandr Martynyuk, Oleksandr Drozd, Ahmesh Tamim, Bui Van Thuong, A. Sachenko, H. Mykhailova, Mykhaylo Dombrovskyi
The paper presents a three-level decomposition model of behavioral on-line testing for distributed information systems of the application level. The model is based on the representation of distributed information systems by a three-tier composition of Petri nets, the identification of reference positions/transitions, recognition of behavioral reference fragments, hierarchical inheritance of the recognized behavior. In Petri nets space-time check models are distinguished, which allow the decomposition of the behavior of a distributed system. The following tasks are solved - definition of distributed information systems analytical models - hierarchical extended Petri nets with structural spatial and temporal decomposition of processes and construction of multi-level analytical models of behavioral online testing of distributed information systems - a multi-level fixed extended behavior of the hierarchical extended Petri nets with additional recognition and encapsulation operations, relations of preordering and inheritance, that are defined on it. Behavioral online testing assumes the preceding definition of recognition of reference positions/transitions, reference fragments and their structures and is applicable for model of project verification and verification of implementations for real distributed information systems.
{"title":"Hierachical Model of Behavior On-line Testing for Distributed Information Systems","authors":"Oleksandr Martynyuk, Oleksandr Drozd, Ahmesh Tamim, Bui Van Thuong, A. Sachenko, H. Mykhailova, Mykhaylo Dombrovskyi","doi":"10.1109/idaacs.2019.8924314","DOIUrl":"https://doi.org/10.1109/idaacs.2019.8924314","url":null,"abstract":"The paper presents a three-level decomposition model of behavioral on-line testing for distributed information systems of the application level. The model is based on the representation of distributed information systems by a three-tier composition of Petri nets, the identification of reference positions/transitions, recognition of behavioral reference fragments, hierarchical inheritance of the recognized behavior. In Petri nets space-time check models are distinguished, which allow the decomposition of the behavior of a distributed system. The following tasks are solved - definition of distributed information systems analytical models - hierarchical extended Petri nets with structural spatial and temporal decomposition of processes and construction of multi-level analytical models of behavioral online testing of distributed information systems - a multi-level fixed extended behavior of the hierarchical extended Petri nets with additional recognition and encapsulation operations, relations of preordering and inheritance, that are defined on it. Behavioral online testing assumes the preceding definition of recognition of reference positions/transitions, reference fragments and their structures and is applicable for model of project verification and verification of implementations for real distributed information systems.","PeriodicalId":415006,"journal":{"name":"2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123930940","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 : 2019-09-01DOI: 10.1109/IDAACS.2019.8924424
D. Popescu, F. Stoican, L. Ichim, G. Stamatescu, Cristian Dragana
Integration of airborne robotic platforms with networks of intelligent sensor systems on the ground has recently emerged as a robust solution for data collection, analysis and control in various specialised applications. The paper presents a hierarchical structure based on the collaboration between a team of unmanned aerial vehicles and a structure of federated wireless sensor networks for crop monitoring in precision agriculture. Key advantages lay in online data collection and relaying to a central monitoring point while effectively managing network load and latency through optimised UAV trajectories and in situ data processing. The experiments were carried out at the Fundulea National Research Institute where different crops and methods are developed. The results demonstrate the fact that the collaborative UAV-WSN approach implemented in the Romanian project MUWI increases the performances both in precision agriculture and ecological agriculture.
{"title":"Collaborative UAV-WSN System for Data Acquisition and Processing in Agriculture","authors":"D. Popescu, F. Stoican, L. Ichim, G. Stamatescu, Cristian Dragana","doi":"10.1109/IDAACS.2019.8924424","DOIUrl":"https://doi.org/10.1109/IDAACS.2019.8924424","url":null,"abstract":"Integration of airborne robotic platforms with networks of intelligent sensor systems on the ground has recently emerged as a robust solution for data collection, analysis and control in various specialised applications. The paper presents a hierarchical structure based on the collaboration between a team of unmanned aerial vehicles and a structure of federated wireless sensor networks for crop monitoring in precision agriculture. Key advantages lay in online data collection and relaying to a central monitoring point while effectively managing network load and latency through optimised UAV trajectories and in situ data processing. The experiments were carried out at the Fundulea National Research Institute where different crops and methods are developed. The results demonstrate the fact that the collaborative UAV-WSN approach implemented in the Romanian project MUWI increases the performances both in precision agriculture and ecological agriculture.","PeriodicalId":415006,"journal":{"name":"2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123488167","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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