The Experts below are selected from a list of 6696 Experts worldwide ranked by ideXlab platform
Ken-ichi Okada - One of the best experts on this subject based on the ideXlab platform.
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A Triage Training System with Dynamic Scenario Change
2015 IEEE 29th International Conference on Advanced Information Networking and Applications, 2015Co-Authors: Misaki Hagino, Yuki Tayama, Ken-ichi OkadaAbstract:In recent years, the concept of triage in emergency medical care at the time of disaster has attracted wide attention and studies have focused on developing electronic triage systems that can collect and manage information about victims through a network. In order to carry out emergency lifesaving activities at an actual disaster site quickly and accurately, it is essential to do triage training frequently. In this study, we propose a triage training system where a training Scenario may change depending on actions of health care workers. In addition, our system makes triage training more realistic by enabling information sharing between the disaster countermeasures office and a disaster site. The evaluation shows that our system enables to prompt an appropriate action to health care workers even in a case of a Dynamic Scenario change, and to share necessary information with the office accurately.
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AINA - A Triage Training System with Dynamic Scenario Change
2015 IEEE 29th International Conference on Advanced Information Networking and Applications, 2015Co-Authors: Misaki Hagino, Yuki Tayama, Ken-ichi OkadaAbstract:In recent years, the concept of triage in emergency medical care at the time of disaster has attracted wide attention and studies have focused on developing electronic triage systems that can collect and manage information about victims through a network. In order to carry out emergency lifesaving activities at an actual disaster site quickly and accurately, it is essential to do triage training frequently. In this study, we propose a triage training system where a training Scenario may change depending on actions of health care workers. In addition, our system makes triage training more realistic by enabling information sharing between the disaster countermeasures office and a disaster site. The evaluation shows that our system enables to prompt an appropriate action to health care workers even in a case of a Dynamic Scenario change, and to share necessary information with the office accurately.
Misaki Hagino - One of the best experts on this subject based on the ideXlab platform.
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A Triage Training System with Dynamic Scenario Change
2015 IEEE 29th International Conference on Advanced Information Networking and Applications, 2015Co-Authors: Misaki Hagino, Yuki Tayama, Ken-ichi OkadaAbstract:In recent years, the concept of triage in emergency medical care at the time of disaster has attracted wide attention and studies have focused on developing electronic triage systems that can collect and manage information about victims through a network. In order to carry out emergency lifesaving activities at an actual disaster site quickly and accurately, it is essential to do triage training frequently. In this study, we propose a triage training system where a training Scenario may change depending on actions of health care workers. In addition, our system makes triage training more realistic by enabling information sharing between the disaster countermeasures office and a disaster site. The evaluation shows that our system enables to prompt an appropriate action to health care workers even in a case of a Dynamic Scenario change, and to share necessary information with the office accurately.
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AINA - A Triage Training System with Dynamic Scenario Change
2015 IEEE 29th International Conference on Advanced Information Networking and Applications, 2015Co-Authors: Misaki Hagino, Yuki Tayama, Ken-ichi OkadaAbstract:In recent years, the concept of triage in emergency medical care at the time of disaster has attracted wide attention and studies have focused on developing electronic triage systems that can collect and manage information about victims through a network. In order to carry out emergency lifesaving activities at an actual disaster site quickly and accurately, it is essential to do triage training frequently. In this study, we propose a triage training system where a training Scenario may change depending on actions of health care workers. In addition, our system makes triage training more realistic by enabling information sharing between the disaster countermeasures office and a disaster site. The evaluation shows that our system enables to prompt an appropriate action to health care workers even in a case of a Dynamic Scenario change, and to share necessary information with the office accurately.
Lei Wu - One of the best experts on this subject based on the ideXlab platform.
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Optimal Multi-Timescale Demand Side Scheduling Considering Dynamic Scenarios of Electricity Demand
IEEE Transactions on Smart Grid, 2019Co-Authors: Lei Wu, Feng Zhai, Wenjing Xu, Baofeng Li, Zhijie LiAbstract:In this paper, an optimal multi-timescale demand side scheduling framework, i.e., the combination of week-ahead and day-ahead, for industrial customers is proposed. Different demand side management (DSM) techniques suitable for distinct week-ahead and day-ahead timescales cooperate for achieving the overall optimal demand scheduling in the entire multi-timescale frame. Specifically, in the week-ahead scheduling, a Dynamic Scenario generation method is proposed to accurately simulate uncertainties of customer electricity demand time-series during the scheduling horizon, which can represent not only the marginal distribution of possible customer loads at each time instant but also the joint distribution among multiple loads at different time instants. In addition, priorities of various DSM techniques accepted by DSM participants and their willingness are also considered, aiming at mitigating impacts on their normal manufacturing process. With actual historical load data of industrial customers from advanced metering infrastructure system, the Dynamic Scenario generation method is shown to be effective in preserving statistic features of load fluctuations, and the proposed optimal multi-timescale coordinated demand side scheduling model is demonstrated to be an effective DSM approach.
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Stochastic Co-Optimization of Midterm and Short-Term Maintenance Outage Scheduling Considering Covariates in Power Systems
IEEE Transactions on Power Systems, 2016Co-Authors: Yifei Wang, Zhiyi Li, Mohammad Shahidehpour, Lei WuAbstract:This paper proposes an integrated framework based on covariates, which coordinates short-term generation and transmission maintenance scheduling with midterm maintenance decisions by considering the effects of short-term security-constrained unit commitment (SCUC). A recursive sampling method is introduced in the proposed Monte Carlo-based framework for generating Scenarios, in which the effects of component aging and covariates on the outage process are quantified by the proportional hazard model (PHM). For each sampled Scenario, an iterative Dynamic Scenario updating approach is introduced to consider interactions among covariate conditions, random component outages, and maintenance outage scheduling. The co-optimization problem is decoupled into three separate optimization subproblems by Lagrangian relaxation (LR), which include generation maintenance scheduling, transmission maintenance scheduling, and short-term SCUC problems. Each Scenario is Dynamically updated based on the optimal maintenance outage and SCUC solutions, and maintenance and SCUC solutions are re-optimized using the updated Scenario. The iterative procedure stops when neither the optimal schedule nor the Dynamic Scenario changes any further. The overall convergence of the proposed Monte Carlo-based framework is checked by the coefficient of variation (CV) of costs over multiple Scenarios. Case studies on the 6-bus system and the IEEE 118-bus system are used to exhibit the effectiveness of proposed framework.
Yuki Tayama - One of the best experts on this subject based on the ideXlab platform.
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A Triage Training System with Dynamic Scenario Change
2015 IEEE 29th International Conference on Advanced Information Networking and Applications, 2015Co-Authors: Misaki Hagino, Yuki Tayama, Ken-ichi OkadaAbstract:In recent years, the concept of triage in emergency medical care at the time of disaster has attracted wide attention and studies have focused on developing electronic triage systems that can collect and manage information about victims through a network. In order to carry out emergency lifesaving activities at an actual disaster site quickly and accurately, it is essential to do triage training frequently. In this study, we propose a triage training system where a training Scenario may change depending on actions of health care workers. In addition, our system makes triage training more realistic by enabling information sharing between the disaster countermeasures office and a disaster site. The evaluation shows that our system enables to prompt an appropriate action to health care workers even in a case of a Dynamic Scenario change, and to share necessary information with the office accurately.
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AINA - A Triage Training System with Dynamic Scenario Change
2015 IEEE 29th International Conference on Advanced Information Networking and Applications, 2015Co-Authors: Misaki Hagino, Yuki Tayama, Ken-ichi OkadaAbstract:In recent years, the concept of triage in emergency medical care at the time of disaster has attracted wide attention and studies have focused on developing electronic triage systems that can collect and manage information about victims through a network. In order to carry out emergency lifesaving activities at an actual disaster site quickly and accurately, it is essential to do triage training frequently. In this study, we propose a triage training system where a training Scenario may change depending on actions of health care workers. In addition, our system makes triage training more realistic by enabling information sharing between the disaster countermeasures office and a disaster site. The evaluation shows that our system enables to prompt an appropriate action to health care workers even in a case of a Dynamic Scenario change, and to share necessary information with the office accurately.
Yijun Yang - One of the best experts on this subject based on the ideXlab platform.
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Unmanned Aerial Vehicle-Aided Communications: Joint Transmit Power and Trajectory Optimization
IEEE Wireless Communications Letters, 2018Co-Authors: Haichao Wang, Jin Chen, Guoru Ding, Yijun YangAbstract:This letter investigates the transmit power and trajectory optimization problem for unmanned aerial vehicle (UAV)-aided networks. Different from majority of the existing studies with fixed communication infrastructure, a Dynamic Scenario is considered where a flying UAV provides wireless services for multiple ground nodes simultaneously. To fully exploit the controllable channel variations provided by the UAV's mobility, the UAV's transmit power and trajectory are jointly optimized to maximize the minimum average throughput within a given time length. For the formulated non-convex optimization with power budget and trajectory constraints, this letter presents an efficient joint transmit power and trajectory optimization algorithm. Simulation results validate the effectiveness of the proposed algorithm and reveal that the optimized transmit power shows a water-filling characteristic in spatial domain.
