BACKGROUND:Rapid on-site triage is critical after mass-casualty incidents(MCIs)and other mass injury events.Unmanned aerial vehicles(UAVs)have been used in MCIs to search and rescue wounded individuals,but they mainly...BACKGROUND:Rapid on-site triage is critical after mass-casualty incidents(MCIs)and other mass injury events.Unmanned aerial vehicles(UAVs)have been used in MCIs to search and rescue wounded individuals,but they mainly depend on the UAV operator’s experience.We used UAVs and artificial intelligence(AI)to provide a new technique for the triage of MCIs and more efficient solutions for emergency rescue.METHODS:This was a preliminary experimental study.We developed an intelligent triage system based on two AI algorithms,namely OpenPose and YOLO.Volunteers were recruited to simulate the MCI scene and triage,combined with UAV and Fifth Generation(5G)Mobile Communication Technology real-time transmission technique,to achieve triage in the simulated MCI scene.RESULTS:Seven postures were designed and recognized to achieve brief but meaningful triage in MCIs.Eight volunteers participated in the MCI simulation scenario.The results of simulation scenarios showed that the proposed method was feasible in tasks of triage for MCIs.CONCLUSION:The proposed technique may provide an alternative technique for the triage of MCIs and is an innovative method in emergency rescue.展开更多
The utility model discloses a new type of flat vehicle for emergency patient transfer, comprising a support frame, a bed board, an infusion stand and a transfer assembly;the transfer assembly comprises a trapezoidal b...The utility model discloses a new type of flat vehicle for emergency patient transfer, comprising a support frame, a bed board, an infusion stand and a transfer assembly;the transfer assembly comprises a trapezoidal base fixed on the upper part of the support frame, and a trapezoid mounted on the bottom or side of the bed board. A sliding sleeve, a limit pin, a fixing cylinder and a spring;the trapezoidal sliding sleeve is matched on the trapezoidal base, a pin shaft hole is arranged on the trapezoidal base, the fixing cylinder is fixed on the trapezoidal sliding sleeve, and the limit pin is sleeved in the fixing cylinder, The bottom of the limit pin protrudes from the trapezoidal sliding sleeve, and the upper part is provided with a traction rod;the spring is sleeved on the traction rod, and a limiting plate is arranged at intervals on both sides of the trapezoidal base, and the limiting plate is wrapped in the trapezoidal sliding sleeve. The outer end: by setting the transfer component, the bed board is allowed to be fixed, slid and completely disengaged from the support frame, which is convenient for transferring the bed board together with the patient on it during the patient transfer process. It provides convenience for medical staff.展开更多
为了全面展示锂电池剩余电量估算方法的研究进展,本文查阅了Web of science、知网、国家知识产权局等数据库中2013年以来的相关论文和专利,综述了锂电池剩余电量的主流估算方法。针对常用的直接估算的方法(安时积分法、开路电压法和阻...为了全面展示锂电池剩余电量估算方法的研究进展,本文查阅了Web of science、知网、国家知识产权局等数据库中2013年以来的相关论文和专利,综述了锂电池剩余电量的主流估算方法。针对常用的直接估算的方法(安时积分法、开路电压法和阻抗表征)、基于等效电路模型的方法、基于电化学模型的方法和基于人工智能神经网络等的锂电池剩余电量估算方法,本文汇总了各方法的估计误差,结果为安时积分法的最大估计误差可达15%;开路电压法最大估计误差为12.4%;电化学阻抗谱法平均估计误差小于3.8%;卡尔曼滤波法的估计误差小于1%;粒子群滤波法的平均误差可小于1%;基于电化学模型的方法平均误差小于2%;基于神经网络的方法平均误差小于2%;多方法混合和多参量联合估计的方法最大误差小于5%,平均误差小于2.5%。结果表明,卡尔曼滤波法相较于直接估算的方法和其他基于模型的方法,精确度更高且更容易实现;基于神经网络的方法无需对电池模型进行分析即可获得较为准确的结果;多种方法混合使用和利用多种参量修正估算值的方法进一步提高了估算精度。本文还针对电动汽车以及植入式医疗电子设备对于剩余电量估算方法的需求,对比分析了各方法的估算精度、优点、难点及适用电池类型,阐明估算方法的具体应用方案,并展望估算方法在这两个领域的发展方向。本文可为相关领域的研究和从业人员提供全面、详实的锂电池剩余电量估算方法的研究现状及发展方向信息。展开更多
基金Sanming Project of Medicine in Shenzhen(No.SZSM201911007)Shenzhen Stability Support Plan(20200824145152001)。
文摘BACKGROUND:Rapid on-site triage is critical after mass-casualty incidents(MCIs)and other mass injury events.Unmanned aerial vehicles(UAVs)have been used in MCIs to search and rescue wounded individuals,but they mainly depend on the UAV operator’s experience.We used UAVs and artificial intelligence(AI)to provide a new technique for the triage of MCIs and more efficient solutions for emergency rescue.METHODS:This was a preliminary experimental study.We developed an intelligent triage system based on two AI algorithms,namely OpenPose and YOLO.Volunteers were recruited to simulate the MCI scene and triage,combined with UAV and Fifth Generation(5G)Mobile Communication Technology real-time transmission technique,to achieve triage in the simulated MCI scene.RESULTS:Seven postures were designed and recognized to achieve brief but meaningful triage in MCIs.Eight volunteers participated in the MCI simulation scenario.The results of simulation scenarios showed that the proposed method was feasible in tasks of triage for MCIs.CONCLUSION:The proposed technique may provide an alternative technique for the triage of MCIs and is an innovative method in emergency rescue.
文摘The utility model discloses a new type of flat vehicle for emergency patient transfer, comprising a support frame, a bed board, an infusion stand and a transfer assembly;the transfer assembly comprises a trapezoidal base fixed on the upper part of the support frame, and a trapezoid mounted on the bottom or side of the bed board. A sliding sleeve, a limit pin, a fixing cylinder and a spring;the trapezoidal sliding sleeve is matched on the trapezoidal base, a pin shaft hole is arranged on the trapezoidal base, the fixing cylinder is fixed on the trapezoidal sliding sleeve, and the limit pin is sleeved in the fixing cylinder, The bottom of the limit pin protrudes from the trapezoidal sliding sleeve, and the upper part is provided with a traction rod;the spring is sleeved on the traction rod, and a limiting plate is arranged at intervals on both sides of the trapezoidal base, and the limiting plate is wrapped in the trapezoidal sliding sleeve. The outer end: by setting the transfer component, the bed board is allowed to be fixed, slid and completely disengaged from the support frame, which is convenient for transferring the bed board together with the patient on it during the patient transfer process. It provides convenience for medical staff.
文摘为了全面展示锂电池剩余电量估算方法的研究进展,本文查阅了Web of science、知网、国家知识产权局等数据库中2013年以来的相关论文和专利,综述了锂电池剩余电量的主流估算方法。针对常用的直接估算的方法(安时积分法、开路电压法和阻抗表征)、基于等效电路模型的方法、基于电化学模型的方法和基于人工智能神经网络等的锂电池剩余电量估算方法,本文汇总了各方法的估计误差,结果为安时积分法的最大估计误差可达15%;开路电压法最大估计误差为12.4%;电化学阻抗谱法平均估计误差小于3.8%;卡尔曼滤波法的估计误差小于1%;粒子群滤波法的平均误差可小于1%;基于电化学模型的方法平均误差小于2%;基于神经网络的方法平均误差小于2%;多方法混合和多参量联合估计的方法最大误差小于5%,平均误差小于2.5%。结果表明,卡尔曼滤波法相较于直接估算的方法和其他基于模型的方法,精确度更高且更容易实现;基于神经网络的方法无需对电池模型进行分析即可获得较为准确的结果;多种方法混合使用和利用多种参量修正估算值的方法进一步提高了估算精度。本文还针对电动汽车以及植入式医疗电子设备对于剩余电量估算方法的需求,对比分析了各方法的估算精度、优点、难点及适用电池类型,阐明估算方法的具体应用方案,并展望估算方法在这两个领域的发展方向。本文可为相关领域的研究和从业人员提供全面、详实的锂电池剩余电量估算方法的研究现状及发展方向信息。
