Pub Date : 2019-09-23DOI: 10.1109/COASE.2019.8842832
Laetitia V. Monnier, William Z. Bemstein, S. Foufou
The visions of the digital thread and smart manufacturing have boosted the potential of relating downstream data to upstream decisions in design. However, to date, the tools and methods to robustly map across the related data representations is significantly lacking. In response, we propose a mapping technique for standard manufacturing data representations. Specifically, we focus on relating controller data from machining tools in the form of MTConnect, an emerging standard that defines the vocabulary and semantics as well as communications protocols for execution data, and G-Code, the most widely used standard for numerical control (NC) instructions. We evaluate the efficacy of our mapping methodology through an error measurement technique that judges the alignment quality between the two data representations. We then relate the proposed methodology to a case study, that includes verified process plans and real execution data, derived from the Smart Manufacturing Systems Test Bed hosted at the National Institute of Standards and Technology.
{"title":"A proposed mapping method for aligning machine execution data to numerical control code","authors":"Laetitia V. Monnier, William Z. Bemstein, S. Foufou","doi":"10.1109/COASE.2019.8842832","DOIUrl":"https://doi.org/10.1109/COASE.2019.8842832","url":null,"abstract":"The visions of the digital thread and smart manufacturing have boosted the potential of relating downstream data to upstream decisions in design. However, to date, the tools and methods to robustly map across the related data representations is significantly lacking. In response, we propose a mapping technique for standard manufacturing data representations. Specifically, we focus on relating controller data from machining tools in the form of MTConnect, an emerging standard that defines the vocabulary and semantics as well as communications protocols for execution data, and G-Code, the most widely used standard for numerical control (NC) instructions. We evaluate the efficacy of our mapping methodology through an error measurement technique that judges the alignment quality between the two data representations. We then relate the proposed methodology to a case study, that includes verified process plans and real execution data, derived from the Smart Manufacturing Systems Test Bed hosted at the National Institute of Standards and Technology.","PeriodicalId":6695,"journal":{"name":"2019 IEEE 15th International Conference on Automation Science and Engineering (CASE)","volume":"46 1","pages":"66-72"},"PeriodicalIF":0.0,"publicationDate":"2019-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81193400","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 : 2019-09-19DOI: 10.1109/COASE.2019.8842984
Bowen Pang, Xiaolei Xie, B. Heidergott, Yijie Peng
The workload of the outpatient departments in Chinese large hospitals is extremely high. Patients often have to wait for a long time before getting their treatments. It is economically expensive to increase medical staffs including nurses and doctors. Therefore, it is critical to optimize staff planning in the outpatient departments to reduce excessive patient waiting time. A high-fidelity simulation model can accurately capture the features of the outpatient service system. But it is very time-consuming to obtain the optimal staff planning decision only based on the simulation model. A simplified queueing model might lead to an analytical solution for the optimal staff planning problem, but it can not fully capture the feature of the real outpatient service system. We propose to use the outputs of the high-fidelity simulation model to drive the output of the low-fidelity queueing model closer to that of the outpatient service system, and then use the data-driven queueing model to make the staff planning decision. Empirical studies on a major hospital are carried out, which demonstrate the effectiveness and efficiency of our method.
{"title":"optimizing outpatient Department Staffing Level using Multi-Fidelity Models","authors":"Bowen Pang, Xiaolei Xie, B. Heidergott, Yijie Peng","doi":"10.1109/COASE.2019.8842984","DOIUrl":"https://doi.org/10.1109/COASE.2019.8842984","url":null,"abstract":"The workload of the outpatient departments in Chinese large hospitals is extremely high. Patients often have to wait for a long time before getting their treatments. It is economically expensive to increase medical staffs including nurses and doctors. Therefore, it is critical to optimize staff planning in the outpatient departments to reduce excessive patient waiting time. A high-fidelity simulation model can accurately capture the features of the outpatient service system. But it is very time-consuming to obtain the optimal staff planning decision only based on the simulation model. A simplified queueing model might lead to an analytical solution for the optimal staff planning problem, but it can not fully capture the feature of the real outpatient service system. We propose to use the outputs of the high-fidelity simulation model to drive the output of the low-fidelity queueing model closer to that of the outpatient service system, and then use the data-driven queueing model to make the staff planning decision. Empirical studies on a major hospital are carried out, which demonstrate the effectiveness and efficiency of our method.","PeriodicalId":6695,"journal":{"name":"2019 IEEE 15th International Conference on Automation Science and Engineering (CASE)","volume":"31 1","pages":"715-720"},"PeriodicalIF":0.0,"publicationDate":"2019-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72850412","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 : 2019-08-26DOI: 10.1109/COASE.2019.8843200
Guixiu Qiao
This paper presents a vision-based, 6 degree of freedom (DOF) measurement system that can measure robot dynamic motions in real-time. A motorized target is designed as a part of the system to work with a vision-based measurement instrument, providing unique features to stand out from the background and enable the achievement of high accuracy monitored, assessed, and predicted to avoid a costly, unexpected shutdown, or decrease in manufacturing quality and production efficiency. The National Institute of Standards and Technology (NIST) is developing the necessary measurement science to support the monitoring, diagnostics, and prognostics of robot systems by providing intelligence to enhance maintenance and control strategies. The robot accuracy degradation research includes the development of modeling and algorithm for the test method, advanced sensor and target development to accurately measure robot 6 DOF information, and algorithms to analyze the data. This paper focuses on the development of the advanced sensor and target. A use case shows the use of the measurement system on a Universal Robot to support the robot accuracy degradation assessment.
{"title":"Advanced Sensor and Target Development to Support Robot Accuracy Degradation Assessment","authors":"Guixiu Qiao","doi":"10.1109/COASE.2019.8843200","DOIUrl":"https://doi.org/10.1109/COASE.2019.8843200","url":null,"abstract":"This paper presents a vision-based, 6 degree of freedom (DOF) measurement system that can measure robot dynamic motions in real-time. A motorized target is designed as a part of the system to work with a vision-based measurement instrument, providing unique features to stand out from the background and enable the achievement of high accuracy monitored, assessed, and predicted to avoid a costly, unexpected shutdown, or decrease in manufacturing quality and production efficiency. The National Institute of Standards and Technology (NIST) is developing the necessary measurement science to support the monitoring, diagnostics, and prognostics of robot systems by providing intelligence to enhance maintenance and control strategies. The robot accuracy degradation research includes the development of modeling and algorithm for the test method, advanced sensor and target development to accurately measure robot 6 DOF information, and algorithms to analyze the data. This paper focuses on the development of the advanced sensor and target. A use case shows the use of the measurement system on a Universal Robot to support the robot accuracy degradation assessment.","PeriodicalId":6695,"journal":{"name":"2019 IEEE 15th International Conference on Automation Science and Engineering (CASE)","volume":"39 1","pages":"54-59"},"PeriodicalIF":0.0,"publicationDate":"2019-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80941169","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 : 2019-08-25DOI: 10.1109/COASE.2019.8843293
Roy Fox, R. Berenstein, I. Stoica, Ken Goldberg
Control policies for home automation robots can be learned from human demonstrations, and hierarchical control has the potential to reduce the required number of demonstrations. When learning multiple policies for related tasks, demonstrations can be reused between the tasks to further reduce the number of demonstrations needed to learn each new policy. We present HIL-MT, a framework for Multi-Task Hierarchical Imitation Learning, involving a human teacher, a networked Toyota HSR robot, and a cloud-based server that stores demonstrations and trains models. In our experiments, HIL-MT learns a policy for clearing a table of dishes from 11.2 demonstrations on average. Learning to set the table requires 19 new demonstrations when training separately, but only 11.6 new demonstrations when also reusing demonstrations of clearing the table. HIL-MT learns policies for building 3- and 4-level pyramids of glass cups from 8.2 and 5 demonstrations, respectively, but reusing the 3-level demonstrations for learning a 4-level policy only requires 2.7 new demonstrations. These results suggest that learning hierarchical policies for structured domestic tasks can reuse existing demonstrations of related tasks to reduce the need for new demonstrations.
{"title":"Multi-Task Hierarchical Imitation Learning for Home Automation","authors":"Roy Fox, R. Berenstein, I. Stoica, Ken Goldberg","doi":"10.1109/COASE.2019.8843293","DOIUrl":"https://doi.org/10.1109/COASE.2019.8843293","url":null,"abstract":"Control policies for home automation robots can be learned from human demonstrations, and hierarchical control has the potential to reduce the required number of demonstrations. When learning multiple policies for related tasks, demonstrations can be reused between the tasks to further reduce the number of demonstrations needed to learn each new policy. We present HIL-MT, a framework for Multi-Task Hierarchical Imitation Learning, involving a human teacher, a networked Toyota HSR robot, and a cloud-based server that stores demonstrations and trains models. In our experiments, HIL-MT learns a policy for clearing a table of dishes from 11.2 demonstrations on average. Learning to set the table requires 19 new demonstrations when training separately, but only 11.6 new demonstrations when also reusing demonstrations of clearing the table. HIL-MT learns policies for building 3- and 4-level pyramids of glass cups from 8.2 and 5 demonstrations, respectively, but reusing the 3-level demonstrations for learning a 4-level policy only requires 2.7 new demonstrations. These results suggest that learning hierarchical policies for structured domestic tasks can reuse existing demonstrations of related tasks to reduce the need for new demonstrations.","PeriodicalId":6695,"journal":{"name":"2019 IEEE 15th International Conference on Automation Science and Engineering (CASE)","volume":"39 2 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2019-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87674519","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 : 2019-08-21DOI: 10.1109/COASE.2019.8842940
Xiapeng Wu, Dapeng Zhang, Fangbo Qin, De Xu
Automatic high precision assembly of millimeter sized objects is a challenging task. Traditional methods for precision assembly rely on explicit programming with real robot system, and require complex parameter-tuning work. In this paper, we realize deep reinforcement learning of precision insertion skill learning, based on prioritized dueling deep Q-network (DQN). The Q-function is represented by the long short term memory (LSTM) neural network, whose input and output are the raw 6D force-torque feedback and the Q-value, respectively. According to the Q values conditioned on the current state, the skill model selects a 6 degree-of-freedom action from the predefined action set. To accelerate the learning process, the data from demonstrations is used to pre-train the model before the DQN starts. In order to improve the insertion efficiency and safety, insertion step length is modulated based on the instant reward. Our proposed method is validated with the peg-in-hole insertion experiments on a precision assembly robot.
{"title":"Deep Reinforcement Learning of Robotic Precision Insertion Skill Accelerated by Demonstrations","authors":"Xiapeng Wu, Dapeng Zhang, Fangbo Qin, De Xu","doi":"10.1109/COASE.2019.8842940","DOIUrl":"https://doi.org/10.1109/COASE.2019.8842940","url":null,"abstract":"Automatic high precision assembly of millimeter sized objects is a challenging task. Traditional methods for precision assembly rely on explicit programming with real robot system, and require complex parameter-tuning work. In this paper, we realize deep reinforcement learning of precision insertion skill learning, based on prioritized dueling deep Q-network (DQN). The Q-function is represented by the long short term memory (LSTM) neural network, whose input and output are the raw 6D force-torque feedback and the Q-value, respectively. According to the Q values conditioned on the current state, the skill model selects a 6 degree-of-freedom action from the predefined action set. To accelerate the learning process, the data from demonstrations is used to pre-train the model before the DQN starts. In order to improve the insertion efficiency and safety, insertion step length is modulated based on the instant reward. Our proposed method is validated with the peg-in-hole insertion experiments on a precision assembly robot.","PeriodicalId":6695,"journal":{"name":"2019 IEEE 15th International Conference on Automation Science and Engineering (CASE)","volume":"2010 1","pages":"1651-1656"},"PeriodicalIF":0.0,"publicationDate":"2019-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78673057","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 : 2019-08-19DOI: 10.1109/COASE.2019.8842911
Aljaz Kramberger, A. Wolniakowski, Mads Høj Rasmussen, M. Munih, A. Ude, Christian Schlette
Object handling in automated manufacturing processes lacks the flexibility to adapt to rapid production changes on the workshop floor typical for small and medium enterprises. Introduction of new grasping solutions represents a time-consuming process. Therefore in this paper, we present a flexible and cost-efficient grasping solution for adaptation to rapid changes in the production processes. The solution consists of a mechanical fingertip exchange system, which can be installed on various grippers and a fingertip design procedure, for rapid development and testing of tailored fingertips for handling multiple parts. The versatility of the approach was benchmarked during the World Robot Summit 2018 where the proposed solution was applied on several use cases in an automated assembly where precision is of the essence to the manufacturing process.
{"title":"Automatic Fingertip Exchange System for Robotic Grasping in Flexible Production Processes","authors":"Aljaz Kramberger, A. Wolniakowski, Mads Høj Rasmussen, M. Munih, A. Ude, Christian Schlette","doi":"10.1109/COASE.2019.8842911","DOIUrl":"https://doi.org/10.1109/COASE.2019.8842911","url":null,"abstract":"Object handling in automated manufacturing processes lacks the flexibility to adapt to rapid production changes on the workshop floor typical for small and medium enterprises. Introduction of new grasping solutions represents a time-consuming process. Therefore in this paper, we present a flexible and cost-efficient grasping solution for adaptation to rapid changes in the production processes. The solution consists of a mechanical fingertip exchange system, which can be installed on various grippers and a fingertip design procedure, for rapid development and testing of tailored fingertips for handling multiple parts. The versatility of the approach was benchmarked during the World Robot Summit 2018 where the proposed solution was applied on several use cases in an automated assembly where precision is of the essence to the manufacturing process.","PeriodicalId":6695,"journal":{"name":"2019 IEEE 15th International Conference on Automation Science and Engineering (CASE)","volume":"15 1","pages":"1664-1669"},"PeriodicalIF":0.0,"publicationDate":"2019-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74805742","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 : 2019-08-01DOI: 10.1109/COASE.2019.8842942
Marco Maggipinto, Gian Antonio Susto, Federico Zocco, S. McLoone
In recent years, Data intensive technologies have become widespread in semiconductor manufacturing. In particular, Virtual Metrology (VM) solutions had proliferated for quality, control and sampling optimization purposes. VM solutions provide estimations of costly measures from already available data, allowing cost reduction and increased throughput. While most of the literature in VM is focused on providing the most accurate methodological approach in terms of prediction accuracy, no work has previously investigated which are the most informative data for VM. This is particularly relevant since literature is divided between VM based on Optical Emission Spectroscopy (OES) and Key Parameter Indicators (KPI) data. In this work we provide a comparison of between VM based on OES and KPIs on a real case study related to a multi-stage modeling problem.
{"title":"What are the Most Informative Data for Virtual Metrology? A use case on Multi-Stage Processes Fault Prediction","authors":"Marco Maggipinto, Gian Antonio Susto, Federico Zocco, S. McLoone","doi":"10.1109/COASE.2019.8842942","DOIUrl":"https://doi.org/10.1109/COASE.2019.8842942","url":null,"abstract":"In recent years, Data intensive technologies have become widespread in semiconductor manufacturing. In particular, Virtual Metrology (VM) solutions had proliferated for quality, control and sampling optimization purposes. VM solutions provide estimations of costly measures from already available data, allowing cost reduction and increased throughput. While most of the literature in VM is focused on providing the most accurate methodological approach in terms of prediction accuracy, no work has previously investigated which are the most informative data for VM. This is particularly relevant since literature is divided between VM based on Optical Emission Spectroscopy (OES) and Key Parameter Indicators (KPI) data. In this work we provide a comparison of between VM based on OES and KPIs on a real case study related to a multi-stage modeling problem.","PeriodicalId":6695,"journal":{"name":"2019 IEEE 15th International Conference on Automation Science and Engineering (CASE)","volume":"77 1","pages":"1796-1801"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74355917","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 : 2019-08-01DOI: 10.1109/coase.2019.8843067
{"title":"CASE 2019 Welcome Message from Program Chair","authors":"","doi":"10.1109/coase.2019.8843067","DOIUrl":"https://doi.org/10.1109/coase.2019.8843067","url":null,"abstract":"","PeriodicalId":6695,"journal":{"name":"2019 IEEE 15th International Conference on Automation Science and Engineering (CASE)","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72647791","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 : 2019-08-01DOI: 10.1109/COASE.2019.8842835
Florens Wasserfall, Daniel Ahlers, N. Hendrich
With 3D-Printing becoming a mainstream technology in industry, the need for online process inspection and quality control arises. In this paper, we propose an approach for optical inspection of fused deposition modeling (FDM) printing with integrated electronics. Our prototype setup combines a traditional FDM extruder with an additional extruder for conductive ink, a vacuum pick-and-place nozzle, and two cameras for object alignment and process control. We describe the camera setup, show typical printing faults encountered on our system, and explain our computer vision algorithms to detect (and repair) those faults. We also describe the integration of the inspection modules into existing slicing- and 3D-printsoftware.
{"title":"Optical In-Situ Verification of 3D-Printed Electronic Circuits","authors":"Florens Wasserfall, Daniel Ahlers, N. Hendrich","doi":"10.1109/COASE.2019.8842835","DOIUrl":"https://doi.org/10.1109/COASE.2019.8842835","url":null,"abstract":"With 3D-Printing becoming a mainstream technology in industry, the need for online process inspection and quality control arises. In this paper, we propose an approach for optical inspection of fused deposition modeling (FDM) printing with integrated electronics. Our prototype setup combines a traditional FDM extruder with an additional extruder for conductive ink, a vacuum pick-and-place nozzle, and two cameras for object alignment and process control. We describe the camera setup, show typical printing faults encountered on our system, and explain our computer vision algorithms to detect (and repair) those faults. We also describe the integration of the inspection modules into existing slicing- and 3D-printsoftware.","PeriodicalId":6695,"journal":{"name":"2019 IEEE 15th International Conference on Automation Science and Engineering (CASE)","volume":"12 1","pages":"1302-1307"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75128232","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 : 2019-08-01DOI: 10.1109/COASE.2019.8843141
Raffaele Carli, M. Dotoli, Vittorio Palmisano
This paper presents a distributed control approach based on game theory for the energy scheduling of demand-side consumers sharing energy production and storage while purchasing further energy from the grid. The interaction between the controllers of consumers’ loads and the manager of shared energy resources is modeled as a two-level game. The competition among consumers is formulated as a noncooperative game, while the interaction between the consumers’ loads and the shared resources manager is formulated as a cooperative game. optimization problems are stated for each player to determine their own optimal strategies. The algorithms for loads controllers and shared resources’ manager are implemented through a distributed approach. Numerical experiments show the effectiveness of the proposed scheme.
{"title":"A Distributed Control Approach Based on Game Theory for the Optimal Energy Scheduling of a Residential Microgrid with Shared Generation and Storage","authors":"Raffaele Carli, M. Dotoli, Vittorio Palmisano","doi":"10.1109/COASE.2019.8843141","DOIUrl":"https://doi.org/10.1109/COASE.2019.8843141","url":null,"abstract":"This paper presents a distributed control approach based on game theory for the energy scheduling of demand-side consumers sharing energy production and storage while purchasing further energy from the grid. The interaction between the controllers of consumers’ loads and the manager of shared energy resources is modeled as a two-level game. The competition among consumers is formulated as a noncooperative game, while the interaction between the consumers’ loads and the shared resources manager is formulated as a cooperative game. optimization problems are stated for each player to determine their own optimal strategies. The algorithms for loads controllers and shared resources’ manager are implemented through a distributed approach. Numerical experiments show the effectiveness of the proposed scheme.","PeriodicalId":6695,"journal":{"name":"2019 IEEE 15th International Conference on Automation Science and Engineering (CASE)","volume":"19 1","pages":"960-965"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77877001","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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