The Experts below are selected from a list of 12993 Experts worldwide ranked by ideXlab platform
Junjie Chen - One of the best experts on this subject based on the ideXlab platform.
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airobsim simulating a multisensor aerial robot for urban search and Rescue Operation and training
Sensors, 2020Co-Authors: Junjie Chen, Donghai LiuAbstract:Unmanned aerial vehicles (UAVs), equipped with a variety of sensors, are being used to provide actionable information to augment first responders' situational awareness in disaster areas for urban search and Rescue (SaR) Operations. However, existing aerial robots are unable to sense the occluded spaces in collapsed structures, and voids buried in disaster rubble that may contain victims. In this study, we developed a framework, AiRobSim, to simulate an aerial robot to acquire both aboveground and underground information for post-disaster SaR. The integration of UAV, ground-penetrating radar (GPR), and other sensors, such as global navigation satellite system (GNSS), inertial measurement unit (IMU), and cameras, enables the aerial robot to provide a holistic view of the complex urban disaster areas. The robot-collected data can help locate critical spaces under the rubble to save trapped victims. The simulation framework can serve as a virtual training platform for novice users to control and operate the robot before actual deployment. Data streams provided by the platform, which include maneuver commands, robot states and environmental information, have potential to facilitate the understanding of the decision-making process in urban SaR and the training of future intelligent SaR robots.
Fei Fang - One of the best experts on this subject based on the ideXlab platform.
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AAAI - Improving Efficiency of Volunteer-Based Food Rescue Operations
2020Co-Authors: Zheyuan Ryan Shi, Yiwen Yuan, Leah Lizarondo, Fei FangAbstract:Food waste and food insecurity are two challenges that coexist in many communities. To mitigate the problem, food Rescue platforms match excess food with the communities in need, and leverage external volunteers to transport the food. However, the external volunteers bring significant uncertainty to the food Rescue Operation. We work with a large food Rescue organization to predict the uncertainty and furthermore to find ways to reduce the human dispatcher's workload and the redundant notifications sent to volunteers. We make two main contributions. (1) We train a stacking model which predicts whether a Rescue will be claimed with high precision and AUC. This model can help the dispatcher better plan for backup options and alleviate their uncertainty. (2) We develop a data-driven optimization algorithm to compute the optimal intervention and notification scheme. The algorithm uses a novel counterfactual data generation approach and the branch and bound framework. Our result reduces the number of notifications and interventions required in the food Rescue Operation. We are working with the organization to deploy our results in the near future.
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Improving Efficiency of Volunteer-Based Food Rescue Operations
Proceedings of the AAAI Conference on Artificial Intelligence, 2020Co-Authors: Zheyuan Ryan Shi, Yiwen Yuan, Leah Lizarondo, Fei FangAbstract:Food waste and food insecurity are two challenges that coexist in many communities. To mitigate the problem, food Rescue platforms match excess food with the communities in need, and leverage external volunteers to transport the food. However, the external volunteers bring significant uncertainty to the food Rescue Operation. We work with a large food Rescue organization to predict the uncertainty and furthermore to find ways to reduce the human dispatcher's workload and the redundant notifications sent to volunteers. We make two main contributions. (1) We train a stacking model which predicts whether a Rescue will be claimed with high precision and AUC. This model can help the dispatcher better plan for backup options and alleviate their uncertainty. (2) We develop a data-driven optimization algorithm to compute the optimal intervention and notification scheme. The algorithm uses a novel counterfactual data generation approach and the branch and bound framework. Our result reduces the number of notifications and interventions required in the food Rescue Operation. We are working with the organization to deploy our results in the near future.
Donghai Liu - One of the best experts on this subject based on the ideXlab platform.
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airobsim simulating a multisensor aerial robot for urban search and Rescue Operation and training
Sensors, 2020Co-Authors: Junjie Chen, Donghai LiuAbstract:Unmanned aerial vehicles (UAVs), equipped with a variety of sensors, are being used to provide actionable information to augment first responders' situational awareness in disaster areas for urban search and Rescue (SaR) Operations. However, existing aerial robots are unable to sense the occluded spaces in collapsed structures, and voids buried in disaster rubble that may contain victims. In this study, we developed a framework, AiRobSim, to simulate an aerial robot to acquire both aboveground and underground information for post-disaster SaR. The integration of UAV, ground-penetrating radar (GPR), and other sensors, such as global navigation satellite system (GNSS), inertial measurement unit (IMU), and cameras, enables the aerial robot to provide a holistic view of the complex urban disaster areas. The robot-collected data can help locate critical spaces under the rubble to save trapped victims. The simulation framework can serve as a virtual training platform for novice users to control and operate the robot before actual deployment. Data streams provided by the platform, which include maneuver commands, robot states and environmental information, have potential to facilitate the understanding of the decision-making process in urban SaR and the training of future intelligent SaR robots.
Zheyuan Ryan Shi - One of the best experts on this subject based on the ideXlab platform.
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AAAI - Improving Efficiency of Volunteer-Based Food Rescue Operations
2020Co-Authors: Zheyuan Ryan Shi, Yiwen Yuan, Leah Lizarondo, Fei FangAbstract:Food waste and food insecurity are two challenges that coexist in many communities. To mitigate the problem, food Rescue platforms match excess food with the communities in need, and leverage external volunteers to transport the food. However, the external volunteers bring significant uncertainty to the food Rescue Operation. We work with a large food Rescue organization to predict the uncertainty and furthermore to find ways to reduce the human dispatcher's workload and the redundant notifications sent to volunteers. We make two main contributions. (1) We train a stacking model which predicts whether a Rescue will be claimed with high precision and AUC. This model can help the dispatcher better plan for backup options and alleviate their uncertainty. (2) We develop a data-driven optimization algorithm to compute the optimal intervention and notification scheme. The algorithm uses a novel counterfactual data generation approach and the branch and bound framework. Our result reduces the number of notifications and interventions required in the food Rescue Operation. We are working with the organization to deploy our results in the near future.
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Improving Efficiency of Volunteer-Based Food Rescue Operations
Proceedings of the AAAI Conference on Artificial Intelligence, 2020Co-Authors: Zheyuan Ryan Shi, Yiwen Yuan, Leah Lizarondo, Fei FangAbstract:Food waste and food insecurity are two challenges that coexist in many communities. To mitigate the problem, food Rescue platforms match excess food with the communities in need, and leverage external volunteers to transport the food. However, the external volunteers bring significant uncertainty to the food Rescue Operation. We work with a large food Rescue organization to predict the uncertainty and furthermore to find ways to reduce the human dispatcher's workload and the redundant notifications sent to volunteers. We make two main contributions. (1) We train a stacking model which predicts whether a Rescue will be claimed with high precision and AUC. This model can help the dispatcher better plan for backup options and alleviate their uncertainty. (2) We develop a data-driven optimization algorithm to compute the optimal intervention and notification scheme. The algorithm uses a novel counterfactual data generation approach and the branch and bound framework. Our result reduces the number of notifications and interventions required in the food Rescue Operation. We are working with the organization to deploy our results in the near future.
Yiwen Yuan - One of the best experts on this subject based on the ideXlab platform.
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AAAI - Improving Efficiency of Volunteer-Based Food Rescue Operations
2020Co-Authors: Zheyuan Ryan Shi, Yiwen Yuan, Leah Lizarondo, Fei FangAbstract:Food waste and food insecurity are two challenges that coexist in many communities. To mitigate the problem, food Rescue platforms match excess food with the communities in need, and leverage external volunteers to transport the food. However, the external volunteers bring significant uncertainty to the food Rescue Operation. We work with a large food Rescue organization to predict the uncertainty and furthermore to find ways to reduce the human dispatcher's workload and the redundant notifications sent to volunteers. We make two main contributions. (1) We train a stacking model which predicts whether a Rescue will be claimed with high precision and AUC. This model can help the dispatcher better plan for backup options and alleviate their uncertainty. (2) We develop a data-driven optimization algorithm to compute the optimal intervention and notification scheme. The algorithm uses a novel counterfactual data generation approach and the branch and bound framework. Our result reduces the number of notifications and interventions required in the food Rescue Operation. We are working with the organization to deploy our results in the near future.
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Improving Efficiency of Volunteer-Based Food Rescue Operations
Proceedings of the AAAI Conference on Artificial Intelligence, 2020Co-Authors: Zheyuan Ryan Shi, Yiwen Yuan, Leah Lizarondo, Fei FangAbstract:Food waste and food insecurity are two challenges that coexist in many communities. To mitigate the problem, food Rescue platforms match excess food with the communities in need, and leverage external volunteers to transport the food. However, the external volunteers bring significant uncertainty to the food Rescue Operation. We work with a large food Rescue organization to predict the uncertainty and furthermore to find ways to reduce the human dispatcher's workload and the redundant notifications sent to volunteers. We make two main contributions. (1) We train a stacking model which predicts whether a Rescue will be claimed with high precision and AUC. This model can help the dispatcher better plan for backup options and alleviate their uncertainty. (2) We develop a data-driven optimization algorithm to compute the optimal intervention and notification scheme. The algorithm uses a novel counterfactual data generation approach and the branch and bound framework. Our result reduces the number of notifications and interventions required in the food Rescue Operation. We are working with the organization to deploy our results in the near future.