基于复杂网络的运动能力在线评估系统设计
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摘要
针对现有的人体运动能力在线评估系统一直存在评估效果差、性能低的问题,提出并设计基于复杂网络的运动能力在线评估系统。通过框架结构设计与评估子系统设计,完成系统的硬件搭建。在软件设计过程中,主要通过对运动评估界限及运动信息库进行设计,并给出整体结构分析情况,结合软硬件部分,完成人体运动能力在线评估系统的设计。实验结果表明,上述系统对于人体运动能力评估的准确性明显高于传统评估系统,且其评估效果较好、性能较高,具有一定的优势。
In allusion to the problems existing in online human exercise ability evaluation system for its poor evaluation effect and low evaluation performance,an online motion ability evaluation system based on complex network is proposed and designed. The architecture structure and evaluation subsystem are designed for hardware establishment of the system. During the process of software design,combined with the hardware part,motion evaluation limits and motion information database are designed and the overall structure analysis is given to accomplish the design of the online human motion ability evaluation system.The experimental results show that the above system has much higher accuracy for human exercise ability evaluation than the traditional evaluation system,and has certain advantages in its evaluation effect and evaluation performance.
In allusion to the problems existing in online human exercise ability evaluation system for its poor evaluation effect and low evaluation performance,an online motion ability evaluation system based on complex network is proposed and designed. The architecture structure and evaluation subsystem are designed for hardware establishment of the system. During the process of software design,combined with the hardware part,motion evaluation limits and motion information database are designed and the overall structure analysis is given to accomplish the design of the online human motion ability evaluation system.The experimental results show that the above system has much higher accuracy for human exercise ability evaluation than the traditional evaluation system,and has certain advantages in its evaluation effect and evaluation performance.
引文
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[5]雷亚国,贾峰,周昕,等.基于深度学习理论的机械装备大数据健康监测方法[J].机械工程学报,2015,51(21):49-56.LEI Yaguo,JIA Feng,ZHOU Xin,et al.A deep learningbased method for machinery health monitoring with big data[J].Journal of mechanical engineering,2015,51(21):49-56.
[6]邓波,陆颖隽,王如志.一种基于CRO的高阶神经网络多示例学习方法[J].计算机科学,2017,44(3):264-267.DENG Bo,LU Yingjun,WANG Ruzhi.Multiple-instance learning method based on CRO high order neural networks[J].Computer science,2017,44(3):264-267.
[7]吴瑀倩,李静,吴晓舟,等.基于深度信念网络的民航发动机状态监测[J].计算机测量与控制,2017,25(7):28-31.WU Yuqian,LI Jing,WU Xiaozhou,et al.Civil aviation engine health condition monitoring based on DBN deep learning theory[J].Computer measurement&control,2017,25(7):28-31.
[8]韩书键,孔辉,顾永环.基于复杂网络的电子对抗情报保障效能评估指标体系[J].国防科技,2015,36(2):38-42.HAN Shujian,KONG Hui,GU Yonghuan.Index system of electronic countermeasure intelligence assurance system effect evaluation based on complex network[J].National defense science&technology,2015,36(2):38-42.
[9]吴辉,彭敏放,张海艳,等.基于复杂网络理论的配电网节点脆弱度评估[J].复杂系统与复杂性科学,2017,14(1):38-45.WU Hui,PENG Minfang,ZHANG Haiyan,et al.Node vulnerability assessment for distribution network based on complex network theory[J].Complex systems and complexity science,2017,14(1):38-45.
[10]郑文强,陈云翔,庄骏,等.基于复杂网络理论的航材配送网络抗毁性分析[J].火力与指挥控制,2015(2):128-132.ZHENG Wenqiang,CHEN Yunxiang,ZHUANG Jun.Invulnerability analysis of aircraft material distribution network based on complex network theory[J].Fire control&command control,2015(2):128-132.