Pub Date : 2012-09-01DOI: 10.1109/CCSII.2012.6470511
U. Mukhaiyar, U. S. Pasaribu
A new approach of identifying stationarity of the space-time processes through the Invers of Autocovariance Matrix (IAcM) is proposed. In particular, we consider the first order Generalized Space Time Autoregressive (GSTAR(1;1)) model. This model is considered to be more representative model in space-time modeling due to its realistic assumption on the uniqueness of spatial location. We are exploring the behavior of the IAcM on behalf of the process stationarity. The stationary condition is a must for GSTAR process to be able to apply in space-time modeling. We obtain that the IAcM may be stated as the function of autoregressive parameters and weight spatial. For the confirmation we carry out numerical analysis for various autoregressive parameter matrices and weight matrices. Through some simulations, we illustrate how significant the autoregressive parameters and weight spatial matrices influence the behavior of the IAcM.
{"title":"The use of IAcM to identify stationarity of the generalized STAR models","authors":"U. Mukhaiyar, U. S. Pasaribu","doi":"10.1109/CCSII.2012.6470511","DOIUrl":"https://doi.org/10.1109/CCSII.2012.6470511","url":null,"abstract":"A new approach of identifying stationarity of the space-time processes through the Invers of Autocovariance Matrix (IAcM) is proposed. In particular, we consider the first order Generalized Space Time Autoregressive (GSTAR(1;1)) model. This model is considered to be more representative model in space-time modeling due to its realistic assumption on the uniqueness of spatial location. We are exploring the behavior of the IAcM on behalf of the process stationarity. The stationary condition is a must for GSTAR process to be able to apply in space-time modeling. We obtain that the IAcM may be stated as the function of autoregressive parameters and weight spatial. For the confirmation we carry out numerical analysis for various autoregressive parameter matrices and weight matrices. Through some simulations, we illustrate how significant the autoregressive parameters and weight spatial matrices influence the behavior of the IAcM.","PeriodicalId":389895,"journal":{"name":"2012 IEEE Conference on Control, Systems & Industrial Informatics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128406881","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 : 2012-09-01DOI: 10.1109/CCSII.2012.6470462
Sutrisno, Salmah, I. E. Wijayanti
In this paper, we study the cooperative dynamic game problem for discrete time case. We solved this problem by determining Pareto solution, continued by finding Nash-bargaining solution. We assume the difference equation in this problem is linear and time invariant. The objective function for each player has the quadratic form and positive definite. We can proof that Pareto solution can be determined by minimizing linear convex combination of all objective functions. The disagreement point of all players is obtained by finding minimax point. The Nash-bargaining solution is selecting a point in Pareto frontier such that the product of utility gains from disagreement point is maximal.
{"title":"Cooperative dynamic game, discrete time case","authors":"Sutrisno, Salmah, I. E. Wijayanti","doi":"10.1109/CCSII.2012.6470462","DOIUrl":"https://doi.org/10.1109/CCSII.2012.6470462","url":null,"abstract":"In this paper, we study the cooperative dynamic game problem for discrete time case. We solved this problem by determining Pareto solution, continued by finding Nash-bargaining solution. We assume the difference equation in this problem is linear and time invariant. The objective function for each player has the quadratic form and positive definite. We can proof that Pareto solution can be determined by minimizing linear convex combination of all objective functions. The disagreement point of all players is obtained by finding minimax point. The Nash-bargaining solution is selecting a point in Pareto frontier such that the product of utility gains from disagreement point is maximal.","PeriodicalId":389895,"journal":{"name":"2012 IEEE Conference on Control, Systems & Industrial Informatics","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134067292","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 : 2012-09-01DOI: 10.1109/CCSII.2012.6470485
I. Idris, Muhammad Ikhsan Sani
Aeroponic is the method of cultivating plants inside a conditioned air environment without the use of soil or water medium. This method grows plants suspended in a closed or semi-closed environment (chamber) by spraying the plant's dangling roots and lower stems with nutrient-rich solution. Under this circumstances, controlled environment has a strong potential to improve plants' developmental stages, health, and growth. In the recent decade, aeroponic system is applied intensively for the purpose of growing potato in order to produce disease-free potato seed and in order to have a pesticides-free cultivation environment. It is also predicted that the aeroponic method will be able to lower potato farmers' cost of operation and increase their yields. A monitoring and control system intended for water and nutrients distribution has been designed to support the optimal application of aeroponic cultivation system for seed-potatoes production. The monitoring system was used to monitor the chamber's parameters such as temperature and humidity. meanwhile, the control system was used to manage actuators in delivering water and nutrients. Temperature and humidity data will be displayed on the LCD and be transmitted to computer to facilitate easier monitoring on the plant growing chamber. The overall system has been designed and implemented to receive user-defined setting points by using the keypad. Microcontroller system will automatically regulate actuators for distribution of water (pump and nozzle) and distribution of nutrients (ultrasonic mist maker, fan, and pump). The pH measurement results at various points showed that the nutrient (pH = 5.8) were successfully spread inside the growing chamber. The average measured-temperature and humidity of growing chamber during the water and nutrients delivery process are 19.6 °C and 83.3% RH, respectively, which meet the environmental requirements for potatoes to grow productively. The system was tested in potato seed greenhouse at Vegetables Research Institute (Balitsa), Ministry of Agriculture, in Lembang, West Java, Indonesia.
{"title":"Monitoring and control of aeroponic growing system for potato production","authors":"I. Idris, Muhammad Ikhsan Sani","doi":"10.1109/CCSII.2012.6470485","DOIUrl":"https://doi.org/10.1109/CCSII.2012.6470485","url":null,"abstract":"Aeroponic is the method of cultivating plants inside a conditioned air environment without the use of soil or water medium. This method grows plants suspended in a closed or semi-closed environment (chamber) by spraying the plant's dangling roots and lower stems with nutrient-rich solution. Under this circumstances, controlled environment has a strong potential to improve plants' developmental stages, health, and growth. In the recent decade, aeroponic system is applied intensively for the purpose of growing potato in order to produce disease-free potato seed and in order to have a pesticides-free cultivation environment. It is also predicted that the aeroponic method will be able to lower potato farmers' cost of operation and increase their yields. A monitoring and control system intended for water and nutrients distribution has been designed to support the optimal application of aeroponic cultivation system for seed-potatoes production. The monitoring system was used to monitor the chamber's parameters such as temperature and humidity. meanwhile, the control system was used to manage actuators in delivering water and nutrients. Temperature and humidity data will be displayed on the LCD and be transmitted to computer to facilitate easier monitoring on the plant growing chamber. The overall system has been designed and implemented to receive user-defined setting points by using the keypad. Microcontroller system will automatically regulate actuators for distribution of water (pump and nozzle) and distribution of nutrients (ultrasonic mist maker, fan, and pump). The pH measurement results at various points showed that the nutrient (pH = 5.8) were successfully spread inside the growing chamber. The average measured-temperature and humidity of growing chamber during the water and nutrients delivery process are 19.6 °C and 83.3% RH, respectively, which meet the environmental requirements for potatoes to grow productively. The system was tested in potato seed greenhouse at Vegetables Research Institute (Balitsa), Ministry of Agriculture, in Lembang, West Java, Indonesia.","PeriodicalId":389895,"journal":{"name":"2012 IEEE Conference on Control, Systems & Industrial Informatics","volume":"30 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115862304","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 : 2012-09-01DOI: 10.1109/CCSII.2012.6470499
A. Fatwanto
A new method for translating software requirements to object-oriented model is proposed. Software requirements specified in a natural language using scenario-like format will be transformed into class diagrams and statechart diagrams of Unified Modeling Language using a specific transformation rule. The transformation rule is based on a grammatical analysis, more specifically syntactical analysis, of requirements specification.
{"title":"Software requirements translation from natural language to object-oriented model","authors":"A. Fatwanto","doi":"10.1109/CCSII.2012.6470499","DOIUrl":"https://doi.org/10.1109/CCSII.2012.6470499","url":null,"abstract":"A new method for translating software requirements to object-oriented model is proposed. Software requirements specified in a natural language using scenario-like format will be transformed into class diagrams and statechart diagrams of Unified Modeling Language using a specific transformation rule. The transformation rule is based on a grammatical analysis, more specifically syntactical analysis, of requirements specification.","PeriodicalId":389895,"journal":{"name":"2012 IEEE Conference on Control, Systems & Industrial Informatics","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115021493","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 : 2012-09-01DOI: 10.1109/CCSII.2012.6470482
M. Zeinali, I. Darus
A quarter-car dynamic system has been modeled using Magnetorheological (MR) damper and a parallel structure of fuzzy PID controller to control the semi-active suspension system. The polynomial model is applied to estimate the behavior of MR damper. The MATLAB software and its Simulink environment are used to simulate the model of the system. The results show the use of fuzzy PID controller has successfully reduced the chassis displacement as the driving comfort objectives than PID controller and improved its robustness.
{"title":"Fuzzy PID controller simulation for a quarter-car semi-active suspension system using Magnetorheological damper","authors":"M. Zeinali, I. Darus","doi":"10.1109/CCSII.2012.6470482","DOIUrl":"https://doi.org/10.1109/CCSII.2012.6470482","url":null,"abstract":"A quarter-car dynamic system has been modeled using Magnetorheological (MR) damper and a parallel structure of fuzzy PID controller to control the semi-active suspension system. The polynomial model is applied to estimate the behavior of MR damper. The MATLAB software and its Simulink environment are used to simulate the model of the system. The results show the use of fuzzy PID controller has successfully reduced the chassis displacement as the driving comfort objectives than PID controller and improved its robustness.","PeriodicalId":389895,"journal":{"name":"2012 IEEE Conference on Control, Systems & Industrial Informatics","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124491553","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 : 2012-09-01DOI: 10.1109/CCSII.2012.6470502
H. A. Nugroho, E. Joelianto, N. Puspito
The paper investigates the dynamics behavior of earthquake occurences by using the framework of nonlinear and chaotic systems. The phase space reconstruction approach is used to analyze chaotic time series of earthquake occurrences in Bali and the surrounding region. In addition, the calculations of fractal geometry are performed in the distribution of space and time. The large fractal dimension related to seismicity in space dimension indicates a random distribution of earthquake occurrences and associate with irregular geometry. While in time series, fractal patterns can be used as reference in earthquake preparedness and hazard mitigation. The results show that earthquake behavior has characteristic of deterministic chaos and varying the value of fractal.
{"title":"Characteristics of earthquake occurrences based on chaotic analysis and fractal dimension","authors":"H. A. Nugroho, E. Joelianto, N. Puspito","doi":"10.1109/CCSII.2012.6470502","DOIUrl":"https://doi.org/10.1109/CCSII.2012.6470502","url":null,"abstract":"The paper investigates the dynamics behavior of earthquake occurences by using the framework of nonlinear and chaotic systems. The phase space reconstruction approach is used to analyze chaotic time series of earthquake occurrences in Bali and the surrounding region. In addition, the calculations of fractal geometry are performed in the distribution of space and time. The large fractal dimension related to seismicity in space dimension indicates a random distribution of earthquake occurrences and associate with irregular geometry. While in time series, fractal patterns can be used as reference in earthquake preparedness and hazard mitigation. The results show that earthquake behavior has characteristic of deterministic chaos and varying the value of fractal.","PeriodicalId":389895,"journal":{"name":"2012 IEEE Conference on Control, Systems & Industrial Informatics","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128742202","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 : 2012-09-01DOI: 10.1109/CCSII.2012.6470477
E. Joelianto, A. Sagala
The paper presents swarm tracking control for flocking of multi-agent autonomous helicopter models. The flocking is defined for a triangular shape without a leader and implemented by using swarm tracking concept. The flocking control design consists of two controllers, i.e. the swarm controller that generates a swarm center and the tracking controller that tracks the swarm center. The tracking controller for each agent is designed by using the standard linear quadratic method. The weighting matrices in the performance quadratic index, the proportional-derivative (PD) controller and the parameters of the swarm model determine the transient performances of the flocking formation.
{"title":"Swarm tracking control for flocking of a multi-agent system","authors":"E. Joelianto, A. Sagala","doi":"10.1109/CCSII.2012.6470477","DOIUrl":"https://doi.org/10.1109/CCSII.2012.6470477","url":null,"abstract":"The paper presents swarm tracking control for flocking of multi-agent autonomous helicopter models. The flocking is defined for a triangular shape without a leader and implemented by using swarm tracking concept. The flocking control design consists of two controllers, i.e. the swarm controller that generates a swarm center and the tracking controller that tracks the swarm center. The tracking controller for each agent is designed by using the standard linear quadratic method. The weighting matrices in the performance quadratic index, the proportional-derivative (PD) controller and the parameters of the swarm model determine the transient performances of the flocking formation.","PeriodicalId":389895,"journal":{"name":"2012 IEEE Conference on Control, Systems & Industrial Informatics","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123736076","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 : 2012-09-01DOI: 10.1109/CCSII.2012.6470489
A. G. Risangtuni, Suprijanto, A. Widyotriatmo
Brain Computer Interface (BCI) is a system that directly utilize Electroencephalograph (EEG) signals to control external devices without aid from any limb of the body. BCI system consists of brainwave acquisition, signal processing, feature extraction and classification. A design of BCI system has been developed by using a wireless EEG Emotiv EPOC neuroheadset and OpenViBE. Both of them are open-source system which gives opportunity to develop our BCI system freely. Mu wave is extracted from the acquired brainwaves when the subject imagined hand movement. Mu wave can be obtained on FC5 and FC6, where premotor activities take place, by apply it to a 8 - 13 Hz bandpass filter. Mu wave power which is the square of EEG signal amplitude is extracted to be classified into two different classes. Feature classification is done by using Support Vector Machine (SVM) in offline classification and online training. EEG signal was acquired on three healthy subjects without well training with BCI control. The task of subjects are imaginary movement of right and left hand with stimulation by a left and right arrow on the screen. Configuration for training and testing phase has been successfully done in OpenViBE towards online application. The mean recognition rate in offline testing and single trial classification is 60.63% for right arrow and 45.93% for left arrow on all subjects.
{"title":"Towards online application of wireless EEG-based open platform Brain Computer Interface","authors":"A. G. Risangtuni, Suprijanto, A. Widyotriatmo","doi":"10.1109/CCSII.2012.6470489","DOIUrl":"https://doi.org/10.1109/CCSII.2012.6470489","url":null,"abstract":"Brain Computer Interface (BCI) is a system that directly utilize Electroencephalograph (EEG) signals to control external devices without aid from any limb of the body. BCI system consists of brainwave acquisition, signal processing, feature extraction and classification. A design of BCI system has been developed by using a wireless EEG Emotiv EPOC neuroheadset and OpenViBE. Both of them are open-source system which gives opportunity to develop our BCI system freely. Mu wave is extracted from the acquired brainwaves when the subject imagined hand movement. Mu wave can be obtained on FC5 and FC6, where premotor activities take place, by apply it to a 8 - 13 Hz bandpass filter. Mu wave power which is the square of EEG signal amplitude is extracted to be classified into two different classes. Feature classification is done by using Support Vector Machine (SVM) in offline classification and online training. EEG signal was acquired on three healthy subjects without well training with BCI control. The task of subjects are imaginary movement of right and left hand with stimulation by a left and right arrow on the screen. Configuration for training and testing phase has been successfully done in OpenViBE towards online application. The mean recognition rate in offline testing and single trial classification is 60.63% for right arrow and 45.93% for left arrow on all subjects.","PeriodicalId":389895,"journal":{"name":"2012 IEEE Conference on Control, Systems & Industrial Informatics","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122155084","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 : 2012-09-01DOI: 10.1109/CCSII.2012.6470465
Hanim Mohd Yatim, I. Z. Mat Darus, M. Mohamad
This paper presents an investigation of an intelligence modeling technique for dynamic characterization of a single-link flexible manipulator system. The flexible manipulator system was first modeled using finite difference (FD) method. A bang-bang torque was applied as an input and the dynamic response of the system was investigated. Performance of the algorithm is compared with the manipulator theoretical natural frequencies obtained analytically. Next, a parametric identification of the system is developed using the conventional Least Square (LS) algorithm and the intelligent Genetic Algorithm (GA). Comparative assessment is presented for validation of the model in characterizing the manipulator system in frequency domain. The developed genetic-modeling approach will be used for control design and development in future work.
{"title":"Parametric identification and dynamic characterisation of flexible manipulator system","authors":"Hanim Mohd Yatim, I. Z. Mat Darus, M. Mohamad","doi":"10.1109/CCSII.2012.6470465","DOIUrl":"https://doi.org/10.1109/CCSII.2012.6470465","url":null,"abstract":"This paper presents an investigation of an intelligence modeling technique for dynamic characterization of a single-link flexible manipulator system. The flexible manipulator system was first modeled using finite difference (FD) method. A bang-bang torque was applied as an input and the dynamic response of the system was investigated. Performance of the algorithm is compared with the manipulator theoretical natural frequencies obtained analytically. Next, a parametric identification of the system is developed using the conventional Least Square (LS) algorithm and the intelligent Genetic Algorithm (GA). Comparative assessment is presented for validation of the model in characterizing the manipulator system in frequency domain. The developed genetic-modeling approach will be used for control design and development in future work.","PeriodicalId":389895,"journal":{"name":"2012 IEEE Conference on Control, Systems & Industrial Informatics","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114206247","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 : 2012-09-01DOI: 10.1109/CCSII.2012.6470488
Huong Pei Choo, S. Sahlan, N. Wahab
In this paper, an activated sludge process model is obtained using two nonlinear system identification techniques. These techniques are Nonlinear ARX Modeling and Hammerstein-Wiener Modeling. A set of raw data from an existing activated sludge process, considered as black box, two different models are obtained and compared in terms of its best fit percentage. From the result, it is concluded that the Hammerstein-Wiener Modeling technique yields better result with lower order and best fit of 91.6%.
{"title":"System identification of activated sludge process","authors":"Huong Pei Choo, S. Sahlan, N. Wahab","doi":"10.1109/CCSII.2012.6470488","DOIUrl":"https://doi.org/10.1109/CCSII.2012.6470488","url":null,"abstract":"In this paper, an activated sludge process model is obtained using two nonlinear system identification techniques. These techniques are Nonlinear ARX Modeling and Hammerstein-Wiener Modeling. A set of raw data from an existing activated sludge process, considered as black box, two different models are obtained and compared in terms of its best fit percentage. From the result, it is concluded that the Hammerstein-Wiener Modeling technique yields better result with lower order and best fit of 91.6%.","PeriodicalId":389895,"journal":{"name":"2012 IEEE Conference on Control, Systems & Industrial Informatics","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121499424","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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