With the increase in ocean exploration activities and underwater development,the autonomous underwater vehicle(AUV)has been widely used as a type of underwater automation equipment in the detection of underwater envir...With the increase in ocean exploration activities and underwater development,the autonomous underwater vehicle(AUV)has been widely used as a type of underwater automation equipment in the detection of underwater environments.However,nowadays AUVs generally have drawbacks such as weak endurance,low intelligence,and poor detection ability.The research and implementation of path-planning methods are the premise of AUVs to achieve actual tasks.To improve the underwater operation ability of the AUV,this paper studies the typical problems of path-planning for the ant colony algorithm and the artificial potential field algorithm.In response to the limitations of a single algorithm,an optimization scheme is proposed to improve the artificial potential field ant colony(APF-AC)algorithm.Compared with traditional ant colony and comparative algorithms,the APF-AC reduced the path length by 1.57%and 0.63%(in the simple environment),8.92%and 3.46%(in the complex environment).The iteration time has been reduced by approximately 28.48%and 18.05%(in the simple environment),18.53%and 9.24%(in the complex environment).Finally,the improved APF-AC algorithm has been validated on the AUV platform,and the experiment is consistent with the simulation.Improved APF-AC algorithm can effectively reduce the underwater operation time and overall power consumption of the AUV,and shows a higher safety.展开更多
目的针对医疗设备数据处理速度慢及诊断正确率低等现象,提出一种基于无线射频识别(Radio Frequency Identification,RFID)技术的医疗设备信息技术研究方法。方法利用RFID定位技术,将蚁群算法融入人工智能算法的神经网络模型中,实现对医...目的针对医疗设备数据处理速度慢及诊断正确率低等现象,提出一种基于无线射频识别(Radio Frequency Identification,RFID)技术的医疗设备信息技术研究方法。方法利用RFID定位技术,将蚁群算法融入人工智能算法的神经网络模型中,实现对医疗仪器的准确定位。同时采用基于模糊理论的医疗设备故障诊断模型,将提取的故障特征信号进行信息融合,判断医疗设备有无故障,并经过模糊理论的决策推理后,确定仪器故障的原因。最后采用连续蚁群算法优化人工智能算法中的神经网络模型权值,用RFID系统采集的信号分类强度数据测试算法,训练反射信号的模型,提升该算法的全局搜索效率。结果实验结果表明,改进后的人工智能算法在医疗设备信息的识别跟踪和定位方面,准确率在90%以上,最高可达97%。结论本研究方法能够有效提高医疗设备信息的识别和定位准确度,为提高医疗诊断正确率提供有力支持。展开更多
基金supported by Research Program supported by the National Natural Science Foundation of China(No.62201249)the Jiangsu Agricultural Science and Technology Innovation Fund(No.CX(21)1007)+2 种基金the Open Project of the Zhejiang Provincial Key Laboratory of Crop Harvesting Equipment and Technology(Nos.2021KY03,2021KY04)University-Industry Collaborative Education Program(No.201801166003)the Postgraduate Research&Practice Innovation Program of Jiangsu Province(No.SJCX22_1042).
文摘With the increase in ocean exploration activities and underwater development,the autonomous underwater vehicle(AUV)has been widely used as a type of underwater automation equipment in the detection of underwater environments.However,nowadays AUVs generally have drawbacks such as weak endurance,low intelligence,and poor detection ability.The research and implementation of path-planning methods are the premise of AUVs to achieve actual tasks.To improve the underwater operation ability of the AUV,this paper studies the typical problems of path-planning for the ant colony algorithm and the artificial potential field algorithm.In response to the limitations of a single algorithm,an optimization scheme is proposed to improve the artificial potential field ant colony(APF-AC)algorithm.Compared with traditional ant colony and comparative algorithms,the APF-AC reduced the path length by 1.57%and 0.63%(in the simple environment),8.92%and 3.46%(in the complex environment).The iteration time has been reduced by approximately 28.48%and 18.05%(in the simple environment),18.53%and 9.24%(in the complex environment).Finally,the improved APF-AC algorithm has been validated on the AUV platform,and the experiment is consistent with the simulation.Improved APF-AC algorithm can effectively reduce the underwater operation time and overall power consumption of the AUV,and shows a higher safety.
文摘目的针对医疗设备数据处理速度慢及诊断正确率低等现象,提出一种基于无线射频识别(Radio Frequency Identification,RFID)技术的医疗设备信息技术研究方法。方法利用RFID定位技术,将蚁群算法融入人工智能算法的神经网络模型中,实现对医疗仪器的准确定位。同时采用基于模糊理论的医疗设备故障诊断模型,将提取的故障特征信号进行信息融合,判断医疗设备有无故障,并经过模糊理论的决策推理后,确定仪器故障的原因。最后采用连续蚁群算法优化人工智能算法中的神经网络模型权值,用RFID系统采集的信号分类强度数据测试算法,训练反射信号的模型,提升该算法的全局搜索效率。结果实验结果表明,改进后的人工智能算法在医疗设备信息的识别跟踪和定位方面,准确率在90%以上,最高可达97%。结论本研究方法能够有效提高医疗设备信息的识别和定位准确度,为提高医疗诊断正确率提供有力支持。
