Pub Date : 2018-10-01DOI: 10.1109/CEIT.2018.8751942
U. Ansari, I. Mehedi, A. Bajodah, U. Al-Saggaf
This paper presents the balance control design using Robust Generalized Dynamic Inversion (RGDI) for Rotary Double Inverted Pendulum (RDIP) system. The RGDI control comprised of the particular part and the robust control element. The particular part is responsible to enforce the constraint dynamics based on the attitude deviation functions, and is inverted using Moore-Penrose Generalized Inverse (MPGI) to obtain the control law. An additional robust term based on the concept of sliding mode is integrated to enhance the robust characteristics against system nonlinearities, uncertainties and disturbances. The singularity problem is addressed by incorporating a dynamic scale factor in the expression of MPGI. The proposed RGDI control will guarantee semi-global practically stable angular position tracking of the horizontal rotary arm and the stabilization of the two pendulums at the upright position. Numerical simulations are carried out on the RDIP simulator to analyze the controller performance.
{"title":"Robust Generalized Dynamic Inversion Control for Stabilizing Rotary Double Inverted Pendulum","authors":"U. Ansari, I. Mehedi, A. Bajodah, U. Al-Saggaf","doi":"10.1109/CEIT.2018.8751942","DOIUrl":"https://doi.org/10.1109/CEIT.2018.8751942","url":null,"abstract":"This paper presents the balance control design using Robust Generalized Dynamic Inversion (RGDI) for Rotary Double Inverted Pendulum (RDIP) system. The RGDI control comprised of the particular part and the robust control element. The particular part is responsible to enforce the constraint dynamics based on the attitude deviation functions, and is inverted using Moore-Penrose Generalized Inverse (MPGI) to obtain the control law. An additional robust term based on the concept of sliding mode is integrated to enhance the robust characteristics against system nonlinearities, uncertainties and disturbances. The singularity problem is addressed by incorporating a dynamic scale factor in the expression of MPGI. The proposed RGDI control will guarantee semi-global practically stable angular position tracking of the horizontal rotary arm and the stabilization of the two pendulums at the upright position. Numerical simulations are carried out on the RDIP simulator to analyze the controller performance.","PeriodicalId":357613,"journal":{"name":"2018 6th International Conference on Control Engineering & Information Technology (CEIT)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126681886","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 : 2018-10-01DOI: 10.1109/CEIT.2018.8751895
B. Ataşlar-Ayyıldız, O. Karahan
This study deals with a fractional order PID (FOPID) controller tuned by Cuckoo Search (CS) algorithm for the trajectory tracking control of a highly nonlinear 3 DOF robotic manipulator. For the purpose of comparison, a traditional PID controller is also tuned by CS. In order to optimize the controllers’ parameters, four different time domain cost functions are used. The robustness test of the tuned controllers is also investigated for a different trajectory. Finally, the simulation results reveal that the proposed FOPID controller can not only assure excellent tracking performance in Joint space, but also improves the robustness of the system for the different trajectory.
{"title":"Tuning of Fractional Order PID Controller using CS Algorithm for Trajectory Tracking Control","authors":"B. Ataşlar-Ayyıldız, O. Karahan","doi":"10.1109/CEIT.2018.8751895","DOIUrl":"https://doi.org/10.1109/CEIT.2018.8751895","url":null,"abstract":"This study deals with a fractional order PID (FOPID) controller tuned by Cuckoo Search (CS) algorithm for the trajectory tracking control of a highly nonlinear 3 DOF robotic manipulator. For the purpose of comparison, a traditional PID controller is also tuned by CS. In order to optimize the controllers’ parameters, four different time domain cost functions are used. The robustness test of the tuned controllers is also investigated for a different trajectory. Finally, the simulation results reveal that the proposed FOPID controller can not only assure excellent tracking performance in Joint space, but also improves the robustness of the system for the different trajectory.","PeriodicalId":357613,"journal":{"name":"2018 6th International Conference on Control Engineering & Information Technology (CEIT)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126748393","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 : 2018-10-01DOI: 10.1109/CEIT.2018.8751852
Bilal Tahir, Kamran Amjad, Samar Firdous, M. Mehmood
Public health surveillance by traditional means is a costly and time consuming process. Today, the widespread use of social media has enabled researchers to study different aspects of life such as health, lifestyle, etc. Anonymous postings on these forums enable people to benefit from the collective experience of others facing similar problems. To effectively discern target data from the outliers in a web corpus, an efficient mechanism is required. Traditional approaches such as keyword-based filtering results in the loss of relevant data due to limited vocabulary and lack of contextual information. In this paper, we present a data filtration framework based on Long short-term memory (LSTM) recurrent neural network model for one-class text classification. We compare similarity of regenerated texts using this model for each disease with the original text using Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metric for outlier filtration and classification. Optimal value of ROUGE similarity threshold is determined by introducing an optimization parameter that minimizes the misclassification rate. Leveraging data from three major online health forums, we show that our classification technique outperforms keyword-based filtering and conventional approach of multi-class text classification. Our classification technique can be effectively used for online social networks, search engines, and online recommender systems.
{"title":"Public Health Surveillance System for Online Social Networks using One-Class Text Classification","authors":"Bilal Tahir, Kamran Amjad, Samar Firdous, M. Mehmood","doi":"10.1109/CEIT.2018.8751852","DOIUrl":"https://doi.org/10.1109/CEIT.2018.8751852","url":null,"abstract":"Public health surveillance by traditional means is a costly and time consuming process. Today, the widespread use of social media has enabled researchers to study different aspects of life such as health, lifestyle, etc. Anonymous postings on these forums enable people to benefit from the collective experience of others facing similar problems. To effectively discern target data from the outliers in a web corpus, an efficient mechanism is required. Traditional approaches such as keyword-based filtering results in the loss of relevant data due to limited vocabulary and lack of contextual information. In this paper, we present a data filtration framework based on Long short-term memory (LSTM) recurrent neural network model for one-class text classification. We compare similarity of regenerated texts using this model for each disease with the original text using Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metric for outlier filtration and classification. Optimal value of ROUGE similarity threshold is determined by introducing an optimization parameter that minimizes the misclassification rate. Leveraging data from three major online health forums, we show that our classification technique outperforms keyword-based filtering and conventional approach of multi-class text classification. Our classification technique can be effectively used for online social networks, search engines, and online recommender systems.","PeriodicalId":357613,"journal":{"name":"2018 6th International Conference on Control Engineering & Information Technology (CEIT)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114201610","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 : 2018-10-01DOI: 10.1109/CEIT.2018.8751869
Ismail Koc, E. Arslan
Nowadays, the competition between companies is rapidly increasing in every industry. This leads to companies trying to be prepared for the near future by forecasting business conditions. The estimated success rate in this context directly affects the success rate of the companies. Airline transport in Turkey, which has grown at a higher rate than Europe's, is an important part of the country's economy and transportation infrastructure. Furthermore, airports encourage development by motivating the commercial activities around them. In the competitive environment of airline transportation, successful forecasting is a crucial issue. Different methods such as multiple linear regression analysis, back-propagation neural networks (BPN), gravity models, multimode models, time series models are used in forecasting studies. In this study, an Artificial Neural Network (ANN) model is used for demand forecasting in domestic air transport in Turkey. In the scope of this study, AzureML, RScript and MATLAB were used for the dataset that is gained between 01.01.2007 - 01.11.2015 and some successful results were obtained. Pearson's correlation coefficient is used as the performance criteria for evaluation and it is observed that the results obtained from the proposed model are at an acceptable level which are gained between 0,79 and 0,93. Therefore, the proposed Artificial Neural Network (ANN) model can be used as a demand forecasting in many areas such as capacity planning, airport infrastructure planning, airplane investments in air transportation.
{"title":"Demand Forecasting for Domestic Air Transportation in Turkey using Artificial Neural Networks","authors":"Ismail Koc, E. Arslan","doi":"10.1109/CEIT.2018.8751869","DOIUrl":"https://doi.org/10.1109/CEIT.2018.8751869","url":null,"abstract":"Nowadays, the competition between companies is rapidly increasing in every industry. This leads to companies trying to be prepared for the near future by forecasting business conditions. The estimated success rate in this context directly affects the success rate of the companies. Airline transport in Turkey, which has grown at a higher rate than Europe's, is an important part of the country's economy and transportation infrastructure. Furthermore, airports encourage development by motivating the commercial activities around them. In the competitive environment of airline transportation, successful forecasting is a crucial issue. Different methods such as multiple linear regression analysis, back-propagation neural networks (BPN), gravity models, multimode models, time series models are used in forecasting studies. In this study, an Artificial Neural Network (ANN) model is used for demand forecasting in domestic air transport in Turkey. In the scope of this study, AzureML, RScript and MATLAB were used for the dataset that is gained between 01.01.2007 - 01.11.2015 and some successful results were obtained. Pearson's correlation coefficient is used as the performance criteria for evaluation and it is observed that the results obtained from the proposed model are at an acceptable level which are gained between 0,79 and 0,93. Therefore, the proposed Artificial Neural Network (ANN) model can be used as a demand forecasting in many areas such as capacity planning, airport infrastructure planning, airplane investments in air transportation.","PeriodicalId":357613,"journal":{"name":"2018 6th International Conference on Control Engineering & Information Technology (CEIT)","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122712947","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 : 2018-10-01DOI: 10.1109/CEIT.2018.8751863
Benalia Nadia, Ben Si Ali Nadia, Zerzouri Noura
The problems of voltage stability have aroused the interest of researchers in the electrical system around the world. It is important to maintain the system stability, or else it would lead to voltage collapse and consequently complete blackout of the system. In this paper the voltage stability indices, Fast Voltage Stability Index (FVSI); Line stability index LQP and Line stability index Lmn are used to determine the stability of a system. These indices are used to identify the most critical line of the system. Under single line outage condition, effect of placing a TCSC in the system on FVSI; Lpq index and Lmn index has been observed. An IEEE 14 bus system has been considered for simulation purpose with PSAT/ matlab.
{"title":"Comparison of Line StabilityIndex with TCSC Under Different Cases With PSAT","authors":"Benalia Nadia, Ben Si Ali Nadia, Zerzouri Noura","doi":"10.1109/CEIT.2018.8751863","DOIUrl":"https://doi.org/10.1109/CEIT.2018.8751863","url":null,"abstract":"The problems of voltage stability have aroused the interest of researchers in the electrical system around the world. It is important to maintain the system stability, or else it would lead to voltage collapse and consequently complete blackout of the system. In this paper the voltage stability indices, Fast Voltage Stability Index (FVSI); Line stability index LQP and Line stability index Lmn are used to determine the stability of a system. These indices are used to identify the most critical line of the system. Under single line outage condition, effect of placing a TCSC in the system on FVSI; Lpq index and Lmn index has been observed. An IEEE 14 bus system has been considered for simulation purpose with PSAT/ matlab.","PeriodicalId":357613,"journal":{"name":"2018 6th International Conference on Control Engineering & Information Technology (CEIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128708989","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}
Vehicle autonomy definitionally is the act of processing information gathered from the environment and acting on the decisions formed based on this information. Therefore, any autonomous paradigm can only perform as good as the quality of the information it can understand. Lane identification forms the foundation of many of the autonomous drive and driver-assist technologies. However, current methods are not always reliable, especially under the edge-cases. In this paper, we have experimentally evaluated and extended the state-of-the-art deterministic lane detection methods. Our evaluation provides experimental evidence towards their efficacy in extreme cases: real-data with sharp shadows and varying lighting that is recorded through a camera that has a limited field of view. Experimental results suggest that a method that builds similarly to human perception performs better—with an increase of 32% in its accuracy. Our hypothesis is that autonomous vehicles that can perform even under these extreme conditions will play an important role on the fully autonomous systems.
{"title":"Detecting Road Lanes under Extreme Conditions: A Quantitative Performance Evaluation","authors":"Erkan Adalı, Haydar A. Şeker, Ahmetcan Erdogan, Kadir Haspalamutgil, Furkan Turan, Elif Aksu, Umut Karapinar","doi":"10.1109/CEIT.2018.8751835","DOIUrl":"https://doi.org/10.1109/CEIT.2018.8751835","url":null,"abstract":"Vehicle autonomy definitionally is the act of processing information gathered from the environment and acting on the decisions formed based on this information. Therefore, any autonomous paradigm can only perform as good as the quality of the information it can understand. Lane identification forms the foundation of many of the autonomous drive and driver-assist technologies. However, current methods are not always reliable, especially under the edge-cases. In this paper, we have experimentally evaluated and extended the state-of-the-art deterministic lane detection methods. Our evaluation provides experimental evidence towards their efficacy in extreme cases: real-data with sharp shadows and varying lighting that is recorded through a camera that has a limited field of view. Experimental results suggest that a method that builds similarly to human perception performs better—with an increase of 32% in its accuracy. Our hypothesis is that autonomous vehicles that can perform even under these extreme conditions will play an important role on the fully autonomous systems.","PeriodicalId":357613,"journal":{"name":"2018 6th International Conference on Control Engineering & Information Technology (CEIT)","volume":"2017 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114497867","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 : 2018-10-01DOI: 10.1109/CEIT.2018.8751930
Ebubekir Buber, B. Diri
Deep learning approaches are machine learning methods used in many application fields today. Some core mathematical operations performed in deep learning are suitable to be parallelized. Parallel processing increases the operating speed. Graphical Processing Units (GPU) are used frequently for parallel processing. Parallelization capacities of GPUs are higher than CPUs, because GPUs have far more cores than Central Processing Units (CPUs). In this study, benchmarking tests were performed between CPU and GPU. Tesla k80 GPU and Intel Xeon Gold 6126 CPU was used during tests. A system for classifying Web pages with Recurrent Neural Network (RNN) architecture was used to compare performance during testing. CPUs and GPUs running on the cloud were used in the tests because the amount of hardware needed for the tests was high. During the tests, some hyperparameters were adjusted and the performance values were compared between CPU and GPU. It has been observed that the GPU runs faster than the CPU in all tests performed. In some cases, GPU is 4-5 times faster than CPU, according to the tests performed on GPU server and CPU server. These values can be further increased by using a GPU server with more features.
{"title":"Performance Analysis and CPU vs GPU Comparison for Deep Learning","authors":"Ebubekir Buber, B. Diri","doi":"10.1109/CEIT.2018.8751930","DOIUrl":"https://doi.org/10.1109/CEIT.2018.8751930","url":null,"abstract":"Deep learning approaches are machine learning methods used in many application fields today. Some core mathematical operations performed in deep learning are suitable to be parallelized. Parallel processing increases the operating speed. Graphical Processing Units (GPU) are used frequently for parallel processing. Parallelization capacities of GPUs are higher than CPUs, because GPUs have far more cores than Central Processing Units (CPUs). In this study, benchmarking tests were performed between CPU and GPU. Tesla k80 GPU and Intel Xeon Gold 6126 CPU was used during tests. A system for classifying Web pages with Recurrent Neural Network (RNN) architecture was used to compare performance during testing. CPUs and GPUs running on the cloud were used in the tests because the amount of hardware needed for the tests was high. During the tests, some hyperparameters were adjusted and the performance values were compared between CPU and GPU. It has been observed that the GPU runs faster than the CPU in all tests performed. In some cases, GPU is 4-5 times faster than CPU, according to the tests performed on GPU server and CPU server. These values can be further increased by using a GPU server with more features.","PeriodicalId":357613,"journal":{"name":"2018 6th International Conference on Control Engineering & Information Technology (CEIT)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115201168","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 : 2018-10-01DOI: 10.1109/CEIT.2018.8751770
Mikail Purlu, B. Turkay
This paper presents Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) technique to solve dynamic economic dispatch (DED) problem. The main purpose of DED is to minimize total cost of generation power to take care of the various load demand in each hour. DED problem solution also must provide individual inequality and equality constraints at the same time. The algorithms have been applied to two test system, taking into account transmission losses. The first of the selected systems is 3 unit test system and the second is 10 unit system considering the valve point effect. Simulation results applied on the test systems show that the two algorithms obtained optimal and reliable results compared to the other methods used in the literature.
{"title":"Dynamic Economic Dispatch with Valve Point Effect by Using GA and PSO Algorithm","authors":"Mikail Purlu, B. Turkay","doi":"10.1109/CEIT.2018.8751770","DOIUrl":"https://doi.org/10.1109/CEIT.2018.8751770","url":null,"abstract":"This paper presents Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) technique to solve dynamic economic dispatch (DED) problem. The main purpose of DED is to minimize total cost of generation power to take care of the various load demand in each hour. DED problem solution also must provide individual inequality and equality constraints at the same time. The algorithms have been applied to two test system, taking into account transmission losses. The first of the selected systems is 3 unit test system and the second is 10 unit system considering the valve point effect. Simulation results applied on the test systems show that the two algorithms obtained optimal and reliable results compared to the other methods used in the literature.","PeriodicalId":357613,"journal":{"name":"2018 6th International Conference on Control Engineering & Information Technology (CEIT)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114741616","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 : 2018-10-01DOI: 10.1109/CEIT.2018.8751888
Kemal Uçak, Gülay Öke Günel
In this study, generalized self-tuning regulator (STR) based on support vector regression (SVR) which was previously introduced is deployed to design a state feedback controller so as to control a nonlinear bioreactor system. The parameters of the state feedback controller used in the controller block are adjusted via SVR based parameter estimator and system model blocks. The performance evaluation of the controller has been examined by simulations carried out on a nonlinear bioreactor system.
{"title":"An adaptive state feedback controller based on SVR for nonlinear systems","authors":"Kemal Uçak, Gülay Öke Günel","doi":"10.1109/CEIT.2018.8751888","DOIUrl":"https://doi.org/10.1109/CEIT.2018.8751888","url":null,"abstract":"In this study, generalized self-tuning regulator (STR) based on support vector regression (SVR) which was previously introduced is deployed to design a state feedback controller so as to control a nonlinear bioreactor system. The parameters of the state feedback controller used in the controller block are adjusted via SVR based parameter estimator and system model blocks. The performance evaluation of the controller has been examined by simulations carried out on a nonlinear bioreactor system.","PeriodicalId":357613,"journal":{"name":"2018 6th International Conference on Control Engineering & Information Technology (CEIT)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127239307","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 : 2018-10-01DOI: 10.1109/CEIT.2018.8751762
Cem Atilgan, Özgür Turay Kaymakçi
Over the last decade, the railway industry has a great evolution about signaling system and there is more orientation from the standard railway signaling system to the communicationbased signaling system day to day. Communications-based train control (CBTC) is a very flexible and useful approach to check train activity and track operation. This system basically build upon radio communication to transfer in time and correct train control information.In this paper, we focus on model the all necessary CBTC elements with finite state automata and build CBTC control architecture with decentralized DES and support the existing control architecture with a three-level hierarchy. For the overall system, we show hierarchical consistency and that the closed-loop behavior is non-blocking. This paper gives an overview of the modelling a discrete event system about CBTC and gives control of CBTC.
{"title":"Modelling and Hierarchical Control of CBTC","authors":"Cem Atilgan, Özgür Turay Kaymakçi","doi":"10.1109/CEIT.2018.8751762","DOIUrl":"https://doi.org/10.1109/CEIT.2018.8751762","url":null,"abstract":"Over the last decade, the railway industry has a great evolution about signaling system and there is more orientation from the standard railway signaling system to the communicationbased signaling system day to day. Communications-based train control (CBTC) is a very flexible and useful approach to check train activity and track operation. This system basically build upon radio communication to transfer in time and correct train control information.In this paper, we focus on model the all necessary CBTC elements with finite state automata and build CBTC control architecture with decentralized DES and support the existing control architecture with a three-level hierarchy. For the overall system, we show hierarchical consistency and that the closed-loop behavior is non-blocking. This paper gives an overview of the modelling a discrete event system about CBTC and gives control of CBTC.","PeriodicalId":357613,"journal":{"name":"2018 6th International Conference on Control Engineering & Information Technology (CEIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127002587","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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