Pub Date : 2021-08-23DOI: 10.1109/CASE49439.2021.9551525
J. Yee, C. Y. Low, N. M. Hashim, F. A. Hanapiah, Ching Theng Koh, N. Zakaria, Khairunnisa Johar, Nurul Atiqah Othman
Anomaly detection algorithms have vast applications, from fraud detection in business transactions to rare pattern detection in a production line to help prevent machinery failures. The availability of quantitative clinical data makes a compelling case for using anomaly detection algorithms in clinical settings, for instance, to help prevent diagnosis errors. This work evaluates the feasibility of using Isolation Forest algorithm for detection of spikes in surface electromyography (sEMG) of biceps and muscle resistive force in upper limb spasticity datasets. Results show that the anomaly detection in sEMG data is a good predictor for the occurrence of catch. It could be deployed in rehabilitation robotic systems for injury prevention by linking the anomaly detection to the actuation module exerting force in the system.
{"title":"Systematic Development of Machine for Abnormal Muscle Activity Detection","authors":"J. Yee, C. Y. Low, N. M. Hashim, F. A. Hanapiah, Ching Theng Koh, N. Zakaria, Khairunnisa Johar, Nurul Atiqah Othman","doi":"10.1109/CASE49439.2021.9551525","DOIUrl":"https://doi.org/10.1109/CASE49439.2021.9551525","url":null,"abstract":"Anomaly detection algorithms have vast applications, from fraud detection in business transactions to rare pattern detection in a production line to help prevent machinery failures. The availability of quantitative clinical data makes a compelling case for using anomaly detection algorithms in clinical settings, for instance, to help prevent diagnosis errors. This work evaluates the feasibility of using Isolation Forest algorithm for detection of spikes in surface electromyography (sEMG) of biceps and muscle resistive force in upper limb spasticity datasets. Results show that the anomaly detection in sEMG data is a good predictor for the occurrence of catch. It could be deployed in rehabilitation robotic systems for injury prevention by linking the anomaly detection to the actuation module exerting force in the system.","PeriodicalId":232083,"journal":{"name":"2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)","volume":"46 3 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":"123260391","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/CASE49439.2021.9551597
Marsela Polic, Bruno Maric, M. Orsag
This paper presents an industrial soft robotics application for the autonomous plastering of complex shaped surfaces, using a collaborative industrial manipulator. In the core of the proposed system is the deep learning based soft body modeling, i.e. deformation estimation of the flexible plastering knife tool. The estimation relies on visual feedback and a deep convolution neural network (CNN). The transfer learning approach and specially designed dataset generation procedures were developed in the learning phase. The estimated deformation of the plastering knife is then used to control the knife inclination with respect to the treated surface, as one of the essential control variables in the plastering procedure. The developed system is experimentally validated, including both the CNN based deformation estimation, as well as its performance in the knife inclination control.
{"title":"Soft robotics approach to autonomous plastering","authors":"Marsela Polic, Bruno Maric, M. Orsag","doi":"10.1109/CASE49439.2021.9551597","DOIUrl":"https://doi.org/10.1109/CASE49439.2021.9551597","url":null,"abstract":"This paper presents an industrial soft robotics application for the autonomous plastering of complex shaped surfaces, using a collaborative industrial manipulator. In the core of the proposed system is the deep learning based soft body modeling, i.e. deformation estimation of the flexible plastering knife tool. The estimation relies on visual feedback and a deep convolution neural network (CNN). The transfer learning approach and specially designed dataset generation procedures were developed in the learning phase. The estimated deformation of the plastering knife is then used to control the knife inclination with respect to the treated surface, as one of the essential control variables in the plastering procedure. The developed system is experimentally validated, including both the CNN based deformation estimation, as well as its performance in the knife inclination control.","PeriodicalId":232083,"journal":{"name":"2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)","volume":"10 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":"123389046","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/CASE49439.2021.9551398
Dimuthu D. K. Arachchige, Yue Chen, I. Godage
Snakes are a remarkable evolutionary success story. Numerous snake-inspired robots have been proposed over the years. Soft robotic snakes (SRS), with their continuous and smooth bending capability, can better mimic their biological counterparts' unique characteristics. Prior SRSs are limited to planar operation with a limited number of planar gaits. We propose a novel SRS with spatial bending ability and investigate snake locomotion gaits beyond the planar gaits of the state-of-the-art systems. We derive a complete floating-base kinematic model of the SRS and use the model to derive joint-space trajectories for serpentine and inward/outward rolling locomotion gaits. These gaits are experimentally validated under varying frequency and amplitude of gait cycles. The results qualitatively and quantitatively validate the proposed SRSs' ability to leverage spatial bending to achieve locomotion gaits not possible with current SRS designs.
{"title":"Soft Robotic Snake Locomotion: Modeling and Experimental Assessment","authors":"Dimuthu D. K. Arachchige, Yue Chen, I. Godage","doi":"10.1109/CASE49439.2021.9551398","DOIUrl":"https://doi.org/10.1109/CASE49439.2021.9551398","url":null,"abstract":"Snakes are a remarkable evolutionary success story. Numerous snake-inspired robots have been proposed over the years. Soft robotic snakes (SRS), with their continuous and smooth bending capability, can better mimic their biological counterparts' unique characteristics. Prior SRSs are limited to planar operation with a limited number of planar gaits. We propose a novel SRS with spatial bending ability and investigate snake locomotion gaits beyond the planar gaits of the state-of-the-art systems. We derive a complete floating-base kinematic model of the SRS and use the model to derive joint-space trajectories for serpentine and inward/outward rolling locomotion gaits. These gaits are experimentally validated under varying frequency and amplitude of gait cycles. The results qualitatively and quantitatively validate the proposed SRSs' ability to leverage spatial bending to achieve locomotion gaits not possible with current SRS designs.","PeriodicalId":232083,"journal":{"name":"2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)","volume":"21 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":"126515091","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/CASE49439.2021.9551447
Lennart Puck, Philipp Keller, Tristan Schnell, C. Plasberg, Atanas Tanev, G. Heppner, A. Rönnau, R. Dillmann
Modern robots are mainly controlled by monolithic black-box controllers provided by the individual manufacturers. In research institutions the first version of the Robot Operating System (ROS1) is widely used for different applications. However, ROS1 lacks real-time capable communication. The ongoing development of ROS2 promises to break this paradigm. By employing Data Distribution Service (DDS) as a middleware the modular architecture aims at providing realtime capabilities. This study assesses the current prospects and limitations of ROS2. It gains novel insights towards improved and, in particular, reliable results regarding latencies and jitter. To this end, the allocation and transmission of ROS2 messages is evaluated in an example application for robotic control. An oscilloscope is applied for external validation of the measurements in such a time-synchronized distributed network. The complete application is set up from non-real-time object detection towards real-time control via ROS2 and EtherCAT. An in-depth evaluation of the ROS2 communication stack on a single host and in distributed setups is included. With real-time safe memory allocation and highly privileged ROS2 processes real-time capabilities are ensured.
{"title":"Performance Evaluation of Real-Time ROS2 Robotic Control in a Time-Synchronized Distributed Network","authors":"Lennart Puck, Philipp Keller, Tristan Schnell, C. Plasberg, Atanas Tanev, G. Heppner, A. Rönnau, R. Dillmann","doi":"10.1109/CASE49439.2021.9551447","DOIUrl":"https://doi.org/10.1109/CASE49439.2021.9551447","url":null,"abstract":"Modern robots are mainly controlled by monolithic black-box controllers provided by the individual manufacturers. In research institutions the first version of the Robot Operating System (ROS1) is widely used for different applications. However, ROS1 lacks real-time capable communication. The ongoing development of ROS2 promises to break this paradigm. By employing Data Distribution Service (DDS) as a middleware the modular architecture aims at providing realtime capabilities. This study assesses the current prospects and limitations of ROS2. It gains novel insights towards improved and, in particular, reliable results regarding latencies and jitter. To this end, the allocation and transmission of ROS2 messages is evaluated in an example application for robotic control. An oscilloscope is applied for external validation of the measurements in such a time-synchronized distributed network. The complete application is set up from non-real-time object detection towards real-time control via ROS2 and EtherCAT. An in-depth evaluation of the ROS2 communication stack on a single host and in distributed setups is included. With real-time safe memory allocation and highly privileged ROS2 processes real-time capabilities are ensured.","PeriodicalId":232083,"journal":{"name":"2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)","volume":"24 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":"125924956","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/CASE49439.2021.9551535
Brandon DelSpina, Yu Zhang, Yue Wang
Pharmaceutical manufacturing has strict requirements on the production environment, procedure, and product quality. Current manual operations face challenges in sterility, efficiency, and ergonomics. The introduction of industrial robotic arms can potentially meet biological manufacturing needs regarding ease to clean, high accuracy, and repeatability. This paper represents a benchtop robot and automation system aiming for the manufacturing of prefilled syringes. The system's hardware components include an ISO-certified robot manipulator (Yaskawa GP8), end-effector tools, cap feeding system, and telescoping transfer platform. A control system for the robot manipulator and Arduino is developed with the Robot Operating System-Industrial (ROS-Industrial). An interpolation method is applied in low-level control to realize linear trajectories of the robot's end-effector. Reliable filling, capping, and sealing are demonstrated to produce prefilled 60 mL syringe in 136 secs, Moreover, during a production run of 33 prefilled syringes, 32 are completed, with only one requiring human intervention due to cap misalignment of the syringe with the capping spout.
{"title":"A Benchtop Robot and Automation Solution for Prefilled Syringes in Pharmaceutical Manufacturing","authors":"Brandon DelSpina, Yu Zhang, Yue Wang","doi":"10.1109/CASE49439.2021.9551535","DOIUrl":"https://doi.org/10.1109/CASE49439.2021.9551535","url":null,"abstract":"Pharmaceutical manufacturing has strict requirements on the production environment, procedure, and product quality. Current manual operations face challenges in sterility, efficiency, and ergonomics. The introduction of industrial robotic arms can potentially meet biological manufacturing needs regarding ease to clean, high accuracy, and repeatability. This paper represents a benchtop robot and automation system aiming for the manufacturing of prefilled syringes. The system's hardware components include an ISO-certified robot manipulator (Yaskawa GP8), end-effector tools, cap feeding system, and telescoping transfer platform. A control system for the robot manipulator and Arduino is developed with the Robot Operating System-Industrial (ROS-Industrial). An interpolation method is applied in low-level control to realize linear trajectories of the robot's end-effector. Reliable filling, capping, and sealing are demonstrated to produce prefilled 60 mL syringe in 136 secs, Moreover, during a production run of 33 prefilled syringes, 32 are completed, with only one requiring human intervention due to cap misalignment of the syringe with the capping spout.","PeriodicalId":232083,"journal":{"name":"2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)","volume":"22 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":"127662693","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/CASE49439.2021.9551601
Mengyu Ji, Shih-Fen Cheng
In this paper, we seek to identify an effective management policy that could reduce supply-demand gaps at taxi queues serving high-density locations where demand surges frequently happen. Unlike current industry practice, which relies on broadcasting to attract taxis to come and serve the queue, we propose more proactive and adaptive approaches to handle demand surges. Our design objective is to reduce the cumulative supply-demand gaps as much as we could by sending notifications to individual taxis. To address this problem, we first propose a highly effective passenger demand prediction system that is based on the real-time flight arrival information. By monitoring cumulative passenger arrivals, and control for factors such as the flight's departure cities, we demonstrate that a simple linear regression model can accurately predict the number of passengers joining taxi queues. We then propose an optimal control strategy based on a Markov Decision Process to model the decisions of notifying individual taxis that are at different distances away from the airport. By using a real-world dataset, we demonstrate that an accurate passenger demand prediction system is crucial to the effectiveness of taxi queue management. In our numerical studies based on the real-world data, we observe that our proposed approach of optimal control with demand predictions outperforms the same control strategy, yet with Poisson demand assumption, by 43%. Against the status quo, which has no external control, we could reduce the gap by 23%. These results demonstrate that our proposed methodology has strong real-world potential.
{"title":"Automated Taxi Queue Management at High-Demand Venues","authors":"Mengyu Ji, Shih-Fen Cheng","doi":"10.1109/CASE49439.2021.9551601","DOIUrl":"https://doi.org/10.1109/CASE49439.2021.9551601","url":null,"abstract":"In this paper, we seek to identify an effective management policy that could reduce supply-demand gaps at taxi queues serving high-density locations where demand surges frequently happen. Unlike current industry practice, which relies on broadcasting to attract taxis to come and serve the queue, we propose more proactive and adaptive approaches to handle demand surges. Our design objective is to reduce the cumulative supply-demand gaps as much as we could by sending notifications to individual taxis. To address this problem, we first propose a highly effective passenger demand prediction system that is based on the real-time flight arrival information. By monitoring cumulative passenger arrivals, and control for factors such as the flight's departure cities, we demonstrate that a simple linear regression model can accurately predict the number of passengers joining taxi queues. We then propose an optimal control strategy based on a Markov Decision Process to model the decisions of notifying individual taxis that are at different distances away from the airport. By using a real-world dataset, we demonstrate that an accurate passenger demand prediction system is crucial to the effectiveness of taxi queue management. In our numerical studies based on the real-world data, we observe that our proposed approach of optimal control with demand predictions outperforms the same control strategy, yet with Poisson demand assumption, by 43%. Against the status quo, which has no external control, we could reduce the gap by 23%. These results demonstrate that our proposed methodology has strong real-world potential.","PeriodicalId":232083,"journal":{"name":"2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)","volume":"8 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":"127728534","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/CASE49439.2021.9551671
Tobias Schiele, A. Jansche, T. Bernthaler, Anton Kaiser, Daniela Pfister, Stefan Späth-Stockmeier, C. Hollerith
This work applies two state-of-the-art approaches for semantic and instance segmentation of solder voids in X-ray images. Void segmentation is both: an important task in quality and failure analysis of microelectronic components and a challenge to modern computer vision methods, e.g. convolutional neural networks (CNN). We use a CNN named U-Net to distinguish void pixels from the background by semantic segmentation. For instance segmentation, we evaluate another CNN, namely Mask-RCNN, which allows the identification of distinct voids instead of a simple binary mask. This approach allows to identify, separate, and evaluate overlapping voids or even voids that lie on top of each other. For the examined dataset, the U-Net outperforms the Mask-RCNN. Nevertheless, the result suggests a trade-off: Once the dataset contains more than 20% of overlapping voids area, the Mask-RCNN becomes technically favorable.
{"title":"Comparison of deep learning-based image segmentation methods for the detection of voids in X-ray images of microelectronic components","authors":"Tobias Schiele, A. Jansche, T. Bernthaler, Anton Kaiser, Daniela Pfister, Stefan Späth-Stockmeier, C. Hollerith","doi":"10.1109/CASE49439.2021.9551671","DOIUrl":"https://doi.org/10.1109/CASE49439.2021.9551671","url":null,"abstract":"This work applies two state-of-the-art approaches for semantic and instance segmentation of solder voids in X-ray images. Void segmentation is both: an important task in quality and failure analysis of microelectronic components and a challenge to modern computer vision methods, e.g. convolutional neural networks (CNN). We use a CNN named U-Net to distinguish void pixels from the background by semantic segmentation. For instance segmentation, we evaluate another CNN, namely Mask-RCNN, which allows the identification of distinct voids instead of a simple binary mask. This approach allows to identify, separate, and evaluate overlapping voids or even voids that lie on top of each other. For the examined dataset, the U-Net outperforms the Mask-RCNN. Nevertheless, the result suggests a trade-off: Once the dataset contains more than 20% of overlapping voids area, the Mask-RCNN becomes technically favorable.","PeriodicalId":232083,"journal":{"name":"2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)","volume":"37 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":"133288731","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/CASE49439.2021.9551460
Yuting Sun, Liang Zhang
Production system modeling refers to the process of constructing valid and high-fidelity mathematical models that are capable of capturing the behavior of job flow in the manufacturing systems. During the modeling process, model parameter identification is the most critical step. This step, however, often involves a significant amount of complex and nonstandardized work. To tackle this problem, we propose to reversely compute the production system model parameters based on standard manufacturing system performance metrics. In this paper, we consider a two-machine production line model, in which the machines follow the exponential reliability model and have identical processing speed, and formulate a constrained optimization problem with the objective of finding the optimal machine parameters which can fit the system performance metrics the best. To solve this problem, barrier method with BFGS quasi-Newton algorithm and cyclic coordinate descent method with proximal point update are developed. The accuracy of these two methods in estimating machine parameters and performance metrics are computed and compared through extensive numerical experiments. Although barrier method is much more efficient in terms of computation time, the risk of getting trapped in local optima exists due to the lack of convexity. On the other hand, the numerical experiments demonstrate that the coordinate descent method reaches the global optimal solution for all the cases. Therefore, an ensemble strategy is recommended to ensure a high accuracy in parameter estimation with acceptable computation time.
{"title":"Parameter Identification for Synchronous Two-Machine Exponential Production Line Model","authors":"Yuting Sun, Liang Zhang","doi":"10.1109/CASE49439.2021.9551460","DOIUrl":"https://doi.org/10.1109/CASE49439.2021.9551460","url":null,"abstract":"Production system modeling refers to the process of constructing valid and high-fidelity mathematical models that are capable of capturing the behavior of job flow in the manufacturing systems. During the modeling process, model parameter identification is the most critical step. This step, however, often involves a significant amount of complex and nonstandardized work. To tackle this problem, we propose to reversely compute the production system model parameters based on standard manufacturing system performance metrics. In this paper, we consider a two-machine production line model, in which the machines follow the exponential reliability model and have identical processing speed, and formulate a constrained optimization problem with the objective of finding the optimal machine parameters which can fit the system performance metrics the best. To solve this problem, barrier method with BFGS quasi-Newton algorithm and cyclic coordinate descent method with proximal point update are developed. The accuracy of these two methods in estimating machine parameters and performance metrics are computed and compared through extensive numerical experiments. Although barrier method is much more efficient in terms of computation time, the risk of getting trapped in local optima exists due to the lack of convexity. On the other hand, the numerical experiments demonstrate that the coordinate descent method reaches the global optimal solution for all the cases. Therefore, an ensemble strategy is recommended to ensure a high accuracy in parameter estimation with acceptable computation time.","PeriodicalId":232083,"journal":{"name":"2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)","volume":"41 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":"130298623","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/CASE49439.2021.9551405
Chung-Kyun Han, Shih-Fen Cheng
In recent years we have increasingly seen the movement for the retail industry to move their operations online. Along the process, it has created brand new patterns for the fulfillment service, and the logistics service providers serving these retailers have no choice but to adapt. The most challenging issues faced by all logistics service providers are the highly fluctuating demands and the shortening response times. All these challenges imply that maintaining a fixed fleet will either be too costly or insufficient. One potential solution is to tap into the crowdsourced workforce. However, existing industry practices of relying on human planners or worker's self-planning have been shown to be inefficient and laborious. In this paper, we introduce a centralized planning model for the crowdsourced logistics delivery paradigm, considering individual worker's spatio-temporal preferences. Considering worker's spatio-temporal preferences is important for the planner as it could significantly improve crowdsourced worker's productivity. Our major contributions are in the formulation of the problem as a mixed-integer program and the proposal of an efficient algorithm that is based on the column generation and the Lagrangian relaxation frameworks. Such a hybrid approach allows us to overcome the difficulty encountered separately by the classical column generation and Lagrangian relaxation approaches. By using a series of real-world-inspired numerical instances, we have demonstrated the effectiveness of our approach against classical column generation and Lagrangian relaxation approaches, and a decentralized, agent-centric greedy approach. Our proposed hybrid approach is scalable to large problem instances, with reasonable solution quality, and achieves better allocation fairness.
{"title":"A Lagrangian Column Generation Approach for the Probabilistic Crowdsourced Logistics Planning","authors":"Chung-Kyun Han, Shih-Fen Cheng","doi":"10.1109/CASE49439.2021.9551405","DOIUrl":"https://doi.org/10.1109/CASE49439.2021.9551405","url":null,"abstract":"In recent years we have increasingly seen the movement for the retail industry to move their operations online. Along the process, it has created brand new patterns for the fulfillment service, and the logistics service providers serving these retailers have no choice but to adapt. The most challenging issues faced by all logistics service providers are the highly fluctuating demands and the shortening response times. All these challenges imply that maintaining a fixed fleet will either be too costly or insufficient. One potential solution is to tap into the crowdsourced workforce. However, existing industry practices of relying on human planners or worker's self-planning have been shown to be inefficient and laborious. In this paper, we introduce a centralized planning model for the crowdsourced logistics delivery paradigm, considering individual worker's spatio-temporal preferences. Considering worker's spatio-temporal preferences is important for the planner as it could significantly improve crowdsourced worker's productivity. Our major contributions are in the formulation of the problem as a mixed-integer program and the proposal of an efficient algorithm that is based on the column generation and the Lagrangian relaxation frameworks. Such a hybrid approach allows us to overcome the difficulty encountered separately by the classical column generation and Lagrangian relaxation approaches. By using a series of real-world-inspired numerical instances, we have demonstrated the effectiveness of our approach against classical column generation and Lagrangian relaxation approaches, and a decentralized, agent-centric greedy approach. Our proposed hybrid approach is scalable to large problem instances, with reasonable solution quality, and achieves better allocation fairness.","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":"130333421","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/CASE49439.2021.9551567
Rebecca Clain, Valeria Borodin, Michel Juge, A. Roussy
Nowadays, virtual metrology models for semiconductor manufacturing aim to be scalable. A Virtual Metrology (VM) system is intended to cover a wide spectrum of production contexts. However, due to the large numbers of possible combinations of recipes, tools and chambers, it becomes intractable to model each context separately. This work presents a VM modeling approach based on the paradigm of transfer learning in a fragmented production context. The approach exploits a 2-Dimensional Convolutional Neural Network (2D-CNN) architecture, namely Spatial Pyramid Pooling Network (SPP-net), to perform the transfer learning from source to target domains with input of different sizes. We implemented several transfer learning strategies on a benchmark dataset provided by the Prognostics and Health Management competition in 2016. The main key points of the proposed approach are discussed based on the findings gathered from the numerical analysis.
{"title":"Virtual metrology for semiconductor manufacturing: Focus on transfer learning","authors":"Rebecca Clain, Valeria Borodin, Michel Juge, A. Roussy","doi":"10.1109/CASE49439.2021.9551567","DOIUrl":"https://doi.org/10.1109/CASE49439.2021.9551567","url":null,"abstract":"Nowadays, virtual metrology models for semiconductor manufacturing aim to be scalable. A Virtual Metrology (VM) system is intended to cover a wide spectrum of production contexts. However, due to the large numbers of possible combinations of recipes, tools and chambers, it becomes intractable to model each context separately. This work presents a VM modeling approach based on the paradigm of transfer learning in a fragmented production context. The approach exploits a 2-Dimensional Convolutional Neural Network (2D-CNN) architecture, namely Spatial Pyramid Pooling Network (SPP-net), to perform the transfer learning from source to target domains with input of different sizes. We implemented several transfer learning strategies on a benchmark dataset provided by the Prognostics and Health Management competition in 2016. The main key points of the proposed approach are discussed based on the findings gathered from the numerical analysis.","PeriodicalId":232083,"journal":{"name":"2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)","volume":"76 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":"115328898","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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