Pub Date : 2021-08-23DOI: 10.1109/CASE49439.2021.9551390
A. Gautam, Ankit Soni, V. S. Shekhawat, Sudeept Mohan
Communication in a multi-robot system is vital as it facilitates coordination. The performance of a multi-robot system improves with coordination. Many state-of-the-art approaches ignore intermittent connectivity, which is inevitable due to communication range restrictions. In this paper, the assumption of global communication is dropped, and the robots are restricted to communicate in a pre-specified communication range as in a realistic scenario. A comparative empirical study of five different state-of-the-art approaches which assume that the communication is omnipresent is conducted. The performance of each algorithm is evaluated by varying the communication range with a different sized robot team both in simulation and on a physical multi-robot test-bed. Finally, the impact of communication range restrictions on the performance of the approaches under evaluation is discussed.
{"title":"Multi-Robot Online Terrain Coverage under Communication Range Restrictions – An Empirical Study","authors":"A. Gautam, Ankit Soni, V. S. Shekhawat, Sudeept Mohan","doi":"10.1109/CASE49439.2021.9551390","DOIUrl":"https://doi.org/10.1109/CASE49439.2021.9551390","url":null,"abstract":"Communication in a multi-robot system is vital as it facilitates coordination. The performance of a multi-robot system improves with coordination. Many state-of-the-art approaches ignore intermittent connectivity, which is inevitable due to communication range restrictions. In this paper, the assumption of global communication is dropped, and the robots are restricted to communicate in a pre-specified communication range as in a realistic scenario. A comparative empirical study of five different state-of-the-art approaches which assume that the communication is omnipresent is conducted. The performance of each algorithm is evaluated by varying the communication range with a different sized robot team both in simulation and on a physical multi-robot test-bed. Finally, the impact of communication range restrictions on the performance of the approaches under evaluation is discussed.","PeriodicalId":232083,"journal":{"name":"2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116677362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-23DOI: 10.1109/CASE49439.2021.9551551
Hao Cheng, Bin Lan, Houde Liu, Xueqian Wang, Bin Liang
A novel concept for visual-lengthwise configuration self-estimator (VLC-SE) of continuum robots is presented, using multiple monocular cameras mounted to each end of segments and lengths encoder as input sensors. The proposed approach grounds the improved lengths based kinematic model with Piecewise Polynomial Curvature (PPC) hypothesis, which ensures accurate modelling and avoids the flaws - as discontinuities and singularities. Meanwhile, we discussed the observability of the improved model. We propose to enhance perception by the vision of each segment end, which comes to the concept of Flexible Multi-Camera Bundle Adjustment (FMC-BA). We validate the performance of our approach on the data collected on a snake-like continuum robot. We also share the first continuum robot datasets: CoRo, including vision and arc lengths data, to promote further research. (https://cutt.ly/CoRo_dataset).
{"title":"VLC-SE: Visual-Lengthwise Configuration Self-Estimator of Continuum Robots","authors":"Hao Cheng, Bin Lan, Houde Liu, Xueqian Wang, Bin Liang","doi":"10.1109/CASE49439.2021.9551551","DOIUrl":"https://doi.org/10.1109/CASE49439.2021.9551551","url":null,"abstract":"A novel concept for visual-lengthwise configuration self-estimator (VLC-SE) of continuum robots is presented, using multiple monocular cameras mounted to each end of segments and lengths encoder as input sensors. The proposed approach grounds the improved lengths based kinematic model with Piecewise Polynomial Curvature (PPC) hypothesis, which ensures accurate modelling and avoids the flaws - as discontinuities and singularities. Meanwhile, we discussed the observability of the improved model. We propose to enhance perception by the vision of each segment end, which comes to the concept of Flexible Multi-Camera Bundle Adjustment (FMC-BA). We validate the performance of our approach on the data collected on a snake-like continuum robot. We also share the first continuum robot datasets: CoRo, including vision and arc lengths data, to promote further research. (https://cutt.ly/CoRo_dataset).","PeriodicalId":232083,"journal":{"name":"2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)","volume":"16 5-6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114123933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-23DOI: 10.1109/CASE49439.2021.9551545
Yuanxiang Wang, Cesar Ruiz, Qiang Huang
Geometric accuracy control is critical for precision additive manufacturing (AM). To learn and predict the shape deformation from a limited number of training products, a fabrication-aware convolution learning framework has been developed in our previous work to describe the layer-by-layer fabrication process. This work extends the convolution learning framework to broader categories of 3D geometries by constructively incorporating spherical and polyhedral shapes into a unified model. It is achieved by extending 2D cookie-cutter modeling approach to 3D case and by modeling spatial correlations. Methodologies demonstrated with real case studies show the promise of prescriptive modeling and control of complicated shape quality in AM.
{"title":"Extended Fabrication-Aware Convolution Learning Framework for Predicting 3D Shape Deformation in Additive Manufacturing","authors":"Yuanxiang Wang, Cesar Ruiz, Qiang Huang","doi":"10.1109/CASE49439.2021.9551545","DOIUrl":"https://doi.org/10.1109/CASE49439.2021.9551545","url":null,"abstract":"Geometric accuracy control is critical for precision additive manufacturing (AM). To learn and predict the shape deformation from a limited number of training products, a fabrication-aware convolution learning framework has been developed in our previous work to describe the layer-by-layer fabrication process. This work extends the convolution learning framework to broader categories of 3D geometries by constructively incorporating spherical and polyhedral shapes into a unified model. It is achieved by extending 2D cookie-cutter modeling approach to 3D case and by modeling spatial correlations. Methodologies demonstrated with real case studies show the promise of prescriptive modeling and control of complicated shape quality in AM.","PeriodicalId":232083,"journal":{"name":"2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115359514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-23DOI: 10.1109/CASE49439.2021.9551668
Lawrence Smith, Travis Hainsworth, Zachary Jordan, Xavier Bell, R. MacCurdy
Soft robotic actuators offer a range of attractive features relative to traditional rigid robots including inherently safer human-robot interaction and robustness to unexpected or extreme loading conditions. Soft robots are challenging to design and fabricate, and most actuators are designed by trial and error and fabricated using labor-intensive multi-step casting processes. We present an integrated collection of software tools that address several limitations in the existing design and fabrication workflow for pneumatic soft actuators. We use implicit geometry functions to specify geometry and material distribution, a GUI-based software tool for interactive exploration of computational network representations of these implicit functions, and an automated tool for generating rapid simulation results of candidate designs. We prioritize seamless connectivity between all stages of the design and fabrication process, and elimination of steps that require human intervention. The software tools presented here integrate with existing capabilities for multimaterial additive manufacturing, and are also forward-compatible with emerging automated design techniques. The workflow presented here is intended as a community resource, and aimed at lowering barriers for the discovery of novel soft actuators by experts and novice users. The data gathered from human-interaction with this tool will be used by future automation tools to enable fully-automated soft actuator design based on high-level specifications.
{"title":"A Seamless Workflow for Design and Fabrication of Multimaterial Pneumatic Soft Actuators","authors":"Lawrence Smith, Travis Hainsworth, Zachary Jordan, Xavier Bell, R. MacCurdy","doi":"10.1109/CASE49439.2021.9551668","DOIUrl":"https://doi.org/10.1109/CASE49439.2021.9551668","url":null,"abstract":"Soft robotic actuators offer a range of attractive features relative to traditional rigid robots including inherently safer human-robot interaction and robustness to unexpected or extreme loading conditions. Soft robots are challenging to design and fabricate, and most actuators are designed by trial and error and fabricated using labor-intensive multi-step casting processes. We present an integrated collection of software tools that address several limitations in the existing design and fabrication workflow for pneumatic soft actuators. We use implicit geometry functions to specify geometry and material distribution, a GUI-based software tool for interactive exploration of computational network representations of these implicit functions, and an automated tool for generating rapid simulation results of candidate designs. We prioritize seamless connectivity between all stages of the design and fabrication process, and elimination of steps that require human intervention. The software tools presented here integrate with existing capabilities for multimaterial additive manufacturing, and are also forward-compatible with emerging automated design techniques. The workflow presented here is intended as a community resource, and aimed at lowering barriers for the discovery of novel soft actuators by experts and novice users. The data gathered from human-interaction with this tool will be used by future automation tools to enable fully-automated soft actuator design based on high-level specifications.","PeriodicalId":232083,"journal":{"name":"2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)","volume":"178 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115442976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-23DOI: 10.1109/CASE49439.2021.9551438
B. Lennartson
Bounded Petri nets are in this paper reduced by an incremental abstraction method based on visible bisimulation. An arbitrary bounded Petri net is decomposed into subsystems that are easily transformed to a modular transition system. The basic principle is that places in a Petri net can be interpreted as the synchronous composition of bounded buffers, and a sequence of places can be reduced analytically to a place with extended capacity. Additional restrictions, such as mutual exclusion among shared resources, are formulated as predicates that are easily translated to ordinary transition systems. Since the reduction preserves CTL*-X expressions, it can be used as a stand-alone model checking tool, where temporal properties of the reduced model are easily evaluated. This approach is shown to be very efficient compared to the best known model checking algorithms available in the software tool nuXmv.
{"title":"Incremental Abstraction - An Analytical and Algorithmic Perspective on Petri Net Reduction","authors":"B. Lennartson","doi":"10.1109/CASE49439.2021.9551438","DOIUrl":"https://doi.org/10.1109/CASE49439.2021.9551438","url":null,"abstract":"Bounded Petri nets are in this paper reduced by an incremental abstraction method based on visible bisimulation. An arbitrary bounded Petri net is decomposed into subsystems that are easily transformed to a modular transition system. The basic principle is that places in a Petri net can be interpreted as the synchronous composition of bounded buffers, and a sequence of places can be reduced analytically to a place with extended capacity. Additional restrictions, such as mutual exclusion among shared resources, are formulated as predicates that are easily translated to ordinary transition systems. Since the reduction preserves CTL*-X expressions, it can be used as a stand-alone model checking tool, where temporal properties of the reduced model are easily evaluated. This approach is shown to be very efficient compared to the best known model checking algorithms available in the software tool nuXmv.","PeriodicalId":232083,"journal":{"name":"2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127247870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-23DOI: 10.1109/CASE49439.2021.9551538
J. C. Villumsen, Atsuki Kiuchi, Yuma Shiho, Junko Hosoda, Takahiro Ogura
In supply chains the collaboration between manufacturing companies is crucial to ensure resilient and efficient operation in the face of uncertainty. This is especially true for manufacturers with global supply chains in highly competitive markets with fluctuating demand. We develop a novel approach to sharing of manufacturing capacity in supply chains. The approach is based on combinatorial auctions in which suppliers and buyers submit bids for manufacturing capacity. We present the mixed integer programming formulation of the winner determination problem and evaluates the efficiency and complexity of the approach on several realistic instances. We find that the number of accepted bids increases up to 10-fold compared to a situation without capacity sharing.
{"title":"A Combinatorial Auctions Approach to Capacity Sharing in Collaborative Supply Chains","authors":"J. C. Villumsen, Atsuki Kiuchi, Yuma Shiho, Junko Hosoda, Takahiro Ogura","doi":"10.1109/CASE49439.2021.9551538","DOIUrl":"https://doi.org/10.1109/CASE49439.2021.9551538","url":null,"abstract":"In supply chains the collaboration between manufacturing companies is crucial to ensure resilient and efficient operation in the face of uncertainty. This is especially true for manufacturers with global supply chains in highly competitive markets with fluctuating demand. We develop a novel approach to sharing of manufacturing capacity in supply chains. The approach is based on combinatorial auctions in which suppliers and buyers submit bids for manufacturing capacity. We present the mixed integer programming formulation of the winner determination problem and evaluates the efficiency and complexity of the approach on several realistic instances. We find that the number of accepted bids increases up to 10-fold compared to a situation without capacity sharing.","PeriodicalId":232083,"journal":{"name":"2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124829883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-23DOI: 10.1109/CASE49439.2021.9551675
Shubhankar Shobhit, S. AbhinayN., K. Das
The transportation of long rod-like payload using multiple Multi-Rotor Aerial Vehicles (MRAVs) is analyzed to determine the ideal locations of the anchor points on the payload in order to improve the endurance of the system. The payload is modelled as a beam, with supports as the anchor points of the cable links between the payload and the MRAVs. The criterion for determining the ideal anchor point locations are laid. The proposed method is validated for different types of mass distributions and can be extended to any variation in payload mass distribution transported using multiple MRAVs. The proposed methodology is validated using Hector quadrotor in Gazebo environment.
{"title":"Determination of Anchor Points for Efficient Long Load Transportation using Multi-Rotor Aerial Vehicles","authors":"Shubhankar Shobhit, S. AbhinayN., K. Das","doi":"10.1109/CASE49439.2021.9551675","DOIUrl":"https://doi.org/10.1109/CASE49439.2021.9551675","url":null,"abstract":"The transportation of long rod-like payload using multiple Multi-Rotor Aerial Vehicles (MRAVs) is analyzed to determine the ideal locations of the anchor points on the payload in order to improve the endurance of the system. The payload is modelled as a beam, with supports as the anchor points of the cable links between the payload and the MRAVs. The criterion for determining the ideal anchor point locations are laid. The proposed method is validated for different types of mass distributions and can be extended to any variation in payload mass distribution transported using multiple MRAVs. The proposed methodology is validated using Hector quadrotor in Gazebo environment.","PeriodicalId":232083,"journal":{"name":"2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124859640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-23DOI: 10.1109/CASE49439.2021.9551614
K. Sharma, Simon Kamm, V. Afanasenko, K. M. Barón, I. Kallfass
In power electronic applications, transistors are a vital component. They are, however, susceptible to failures due to degradation of the interconnections and the chip itself. This paper presents a non-destructive approach for failure detection and location in power electronic devices using time-domain reflectometry. The proposed measurement and data generation method is applied to a silicon-carbide power transistor where several characteristics (R, L, C, open, short) and the location of the failure is simulated and characterized. Moreover, the method is also used to find the intrinsic properties of the transistor such as parasitic inductance and capacitance. The data generated is mapped to physical equations, however, the reflected signal of the time-domain reflectometry can be noisy due to multiple discontinuities in the transmission path. Therefore, the simulation and measurement data can be used to train hybrid machine learning models for parameter extraction which automates the failure analysis in Industry4.0 processes to ensure a smart and reliable manufacturing process.
{"title":"Non-Destructive Failure Analysis of Power Devices via Time- Domain Reflectometry","authors":"K. Sharma, Simon Kamm, V. Afanasenko, K. M. Barón, I. Kallfass","doi":"10.1109/CASE49439.2021.9551614","DOIUrl":"https://doi.org/10.1109/CASE49439.2021.9551614","url":null,"abstract":"In power electronic applications, transistors are a vital component. They are, however, susceptible to failures due to degradation of the interconnections and the chip itself. This paper presents a non-destructive approach for failure detection and location in power electronic devices using time-domain reflectometry. The proposed measurement and data generation method is applied to a silicon-carbide power transistor where several characteristics (R, L, C, open, short) and the location of the failure is simulated and characterized. Moreover, the method is also used to find the intrinsic properties of the transistor such as parasitic inductance and capacitance. The data generated is mapped to physical equations, however, the reflected signal of the time-domain reflectometry can be noisy due to multiple discontinuities in the transmission path. Therefore, the simulation and measurement data can be used to train hybrid machine learning models for parameter extraction which automates the failure analysis in Industry4.0 processes to ensure a smart and reliable manufacturing process.","PeriodicalId":232083,"journal":{"name":"2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125185375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-23DOI: 10.1109/CASE49439.2021.9551532
A. Joseph, Juan Wu, Kaiyan Yu, Lan Jiang, N. Cady, Bing Si
Precise and efficient motion prediction and manipulation of micro- and nanoparticles in a complex fluid suspension system under external electric fields has the potential to revolutionize the manufacture of scalable functional nanodevices. However, the physical motion model of the particle based on physical simulation does not consider the effects in the complex fluid suspension system, e.g., boundary conditions, fluid motion, and particle interactions, and often results in imperfect prediction of particle trajectories under the coupled global field. This study proposes a data-driven approach for small-scale particle trajectory prediction by leveraging both physical simulation model and experimental data. Historical function-on-function regression is used to predict experimental trajectories from corresponding simulation trajectories. A gradient boosting algorithm is used for model estimation. Our study is the first-of-its-kind that uses historical function-on-function regression to demonstrate the efficacy of predicting experimental trajectories from simulation trajectories in small-scale particle manipulation under electrical fields, which eventually leads to the design of new automated processes for efficient and smart manufacturing of functional nanodevices towards next-generation neuromorphic computing.
{"title":"Function-on-Function Regression for Trajectory Prediction of Small-Scale Particles towards Next-generation Neuromorphic Computing","authors":"A. Joseph, Juan Wu, Kaiyan Yu, Lan Jiang, N. Cady, Bing Si","doi":"10.1109/CASE49439.2021.9551532","DOIUrl":"https://doi.org/10.1109/CASE49439.2021.9551532","url":null,"abstract":"Precise and efficient motion prediction and manipulation of micro- and nanoparticles in a complex fluid suspension system under external electric fields has the potential to revolutionize the manufacture of scalable functional nanodevices. However, the physical motion model of the particle based on physical simulation does not consider the effects in the complex fluid suspension system, e.g., boundary conditions, fluid motion, and particle interactions, and often results in imperfect prediction of particle trajectories under the coupled global field. This study proposes a data-driven approach for small-scale particle trajectory prediction by leveraging both physical simulation model and experimental data. Historical function-on-function regression is used to predict experimental trajectories from corresponding simulation trajectories. A gradient boosting algorithm is used for model estimation. Our study is the first-of-its-kind that uses historical function-on-function regression to demonstrate the efficacy of predicting experimental trajectories from simulation trajectories in small-scale particle manipulation under electrical fields, which eventually leads to the design of new automated processes for efficient and smart manufacturing of functional nanodevices towards next-generation neuromorphic computing.","PeriodicalId":232083,"journal":{"name":"2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123694546","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-23DOI: 10.1109/CASE49439.2021.9551442
Weiqi Zhang, Chen Zhang, F. Tsung
Passenger flow forecasting is a very critical task for the daily operations of metro system. The rapid development of deep learning methods offers us an opportunity to give an end-to-end solution to system-level prediction. However, complex spatial-temporal correlations of passenger flow data makes it quite challenging. Existing studies tend to model spatial and temporal correlations separately, which may lead to information loss and unsatisfactory prediction performance. Meanwhile, they cannot take full advantage of human knowledge and external information, such as geographical information, metro map information, etc, for modeling. To bridge the research gap, in this study, we propose a well-designed transformer based spatial-temporal fusion network (TSTFN). To cooperate with different types of external information and give additional insights, we first use multiple pre-defined graph structures to construct multi-view GCN for spatial dependence modeling. Then we propose a novel spatial-temporal synchronous self-attention layer to model spatial and temporal correlation simultaneously. Experiments show TSTFN outperforms other state-of-the-art deep learning based methods on both long-term and short-term tasks. The effectiveness of its crucial components has also been verified by using ablation study and analysis.
{"title":"Transformer Based Spatial-Temporal Fusion Network for Metro Passenger Flow Forecasting","authors":"Weiqi Zhang, Chen Zhang, F. Tsung","doi":"10.1109/CASE49439.2021.9551442","DOIUrl":"https://doi.org/10.1109/CASE49439.2021.9551442","url":null,"abstract":"Passenger flow forecasting is a very critical task for the daily operations of metro system. The rapid development of deep learning methods offers us an opportunity to give an end-to-end solution to system-level prediction. However, complex spatial-temporal correlations of passenger flow data makes it quite challenging. Existing studies tend to model spatial and temporal correlations separately, which may lead to information loss and unsatisfactory prediction performance. Meanwhile, they cannot take full advantage of human knowledge and external information, such as geographical information, metro map information, etc, for modeling. To bridge the research gap, in this study, we propose a well-designed transformer based spatial-temporal fusion network (TSTFN). To cooperate with different types of external information and give additional insights, we first use multiple pre-defined graph structures to construct multi-view GCN for spatial dependence modeling. Then we propose a novel spatial-temporal synchronous self-attention layer to model spatial and temporal correlation simultaneously. Experiments show TSTFN outperforms other state-of-the-art deep learning based methods on both long-term and short-term tasks. The effectiveness of its crucial components has also been verified by using ablation study and analysis.","PeriodicalId":232083,"journal":{"name":"2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121595858","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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