为提高海上搜救效率,提出一种基于船舶操纵运动数学模型研究小组(Ship Manoeuvring Mathematical Model Group,MMG)模型的船舶动态扇形自动搜寻模式。在充分考虑风、流等外界扰动影响的基础上,不需要额外进行风、流等漂流模型的推算,以M...为提高海上搜救效率,提出一种基于船舶操纵运动数学模型研究小组(Ship Manoeuvring Mathematical Model Group,MMG)模型的船舶动态扇形自动搜寻模式。在充分考虑风、流等外界扰动影响的基础上,不需要额外进行风、流等漂流模型的推算,以Matlab软件中Simulink模块为工具进行仿真。仿真结果表明:在不依赖GPS船位的前提下,船舶的航向、舵角、航速均符合航海实践要求。该搜寻模式可提高应对海上突发事故的搜救能力,从而减少突发事故中的人员伤亡和财产损失。展开更多
中国五矿与澳大利亚OZ矿业公司的交易17日完成交割。OZ矿业经结算调整获得13．54亿美元资金，而五矿获得OZ矿业大部分核心矿产。在完成收购后，五矿将在澳洲注册成立一家新公司——Minemlsand Mining Group Limited（MMG），以管理开发...中国五矿与澳大利亚OZ矿业公司的交易17日完成交割。OZ矿业经结算调整获得13．54亿美元资金，而五矿获得OZ矿业大部分核心矿产。在完成收购后，五矿将在澳洲注册成立一家新公司——Minemlsand Mining Group Limited（MMG），以管理开发这些资产。展开更多
Mechanomyography (MMG) acquires the oscillatory waves of contracting muscles. Electromyography (EMG) is a tool for monitoring muscle overall electrical activity. During muscle contractions, both techniques can investi...Mechanomyography (MMG) acquires the oscillatory waves of contracting muscles. Electromyography (EMG) is a tool for monitoring muscle overall electrical activity. During muscle contractions, both techniques can investigate the changes that occur in the muscle properties. EMG and MMG parameters have been used for detecting muscle fatigue with diverse test protocols, sensors and filtering. Depending on the analysis window length (WLA), monitoring physiological events could be compromised due to imprecision in the determination of parameters. Therefore, this study investigated the influence of WLA variation on different MMG and EMG parameters during submaximal isometric contractions monitoring MMG and EMG parameters. Ten male volunteers performed isometric contractions of elbow joint. Triaxial accelerometer-based MMG sensor and EMG electrodes were positioned on the biceps brachii muscle belly. Torque was monitored with a load cell. Volunteers remained seated with hip and elbow joint at angles of 110° and 90°, respectively. The protocol consisted in maintaining torque at 70% of maximum voluntary contraction as long as they could. Parameter data of EMG and the modulus of MMG were determined for four segments of the signal. Statistical analysis consisted of analyses of variance and Fisher’s least square differences post-hoc test. Also, Pearson’s correlation was calculated to determine whether parameters that monitor similar physiological events would have strong correlation. The modulus of MMG mean power frequency (MPF) and the number of crossings in the baseline could detect changes between fresh and fatigued muscle with 1.0 s WLA. MPF and the skewness of the spectrum (μ3), parameters related to the compression of the spectrum, behaved differently when monitored with a triaxial MMG sensor. The EMG results show that for the 1.0 s and 2.0 s WLAs have normalized RMS difference with fatigued muscle and that there was strong correlation between parameters of different domains.展开更多
针对上肢肌音信号(Mechanomyography,MMG)动作识别准确率不高的问题,提出一种基于粒子群算法(PSO)与长短期记忆网络相结合的混合模型(Particle Swarm Optimization-Long Short Term Memory,PSO-LSTM)的动作识别方法。采用5通道传感器对...针对上肢肌音信号(Mechanomyography,MMG)动作识别准确率不高的问题,提出一种基于粒子群算法(PSO)与长短期记忆网络相结合的混合模型(Particle Swarm Optimization-Long Short Term Memory,PSO-LSTM)的动作识别方法。采用5通道传感器对受试者进行上肢肌音信号采集,使用巴特沃斯滤波(Butterworth Filter)等方法对肌音信号进行预处理,并进行特征提取;构建基于PSO-LSTM的上肢肌音信号识别模型并进行模型训练和测试;最后从不同测度对比了长短期记忆(LSTM)模型、麻雀搜索算法(Sparrow Search Algorithm,SSA)优化的LSTM模型(Sparrow Search Algorithm-Long Short Term Memory, SSA-LSTM)以及PSO-LSTM模型的实验结果。结果表明,PSO-LSTM模型的准确度均高于LSTM、 SSA-LSTM模型,达到96.9%左右,在迭代损失、迭代速度等方面也优于LSTM、SSA-LSTM模型,从而证明了该模型用于上肢肌音信号识别的优越性。展开更多
文摘为提高海上搜救效率,提出一种基于船舶操纵运动数学模型研究小组(Ship Manoeuvring Mathematical Model Group,MMG)模型的船舶动态扇形自动搜寻模式。在充分考虑风、流等外界扰动影响的基础上,不需要额外进行风、流等漂流模型的推算,以Matlab软件中Simulink模块为工具进行仿真。仿真结果表明:在不依赖GPS船位的前提下,船舶的航向、舵角、航速均符合航海实践要求。该搜寻模式可提高应对海上突发事故的搜救能力,从而减少突发事故中的人员伤亡和财产损失。
基金CNPq and CAPES for the financial support and grants received.
文摘Mechanomyography (MMG) acquires the oscillatory waves of contracting muscles. Electromyography (EMG) is a tool for monitoring muscle overall electrical activity. During muscle contractions, both techniques can investigate the changes that occur in the muscle properties. EMG and MMG parameters have been used for detecting muscle fatigue with diverse test protocols, sensors and filtering. Depending on the analysis window length (WLA), monitoring physiological events could be compromised due to imprecision in the determination of parameters. Therefore, this study investigated the influence of WLA variation on different MMG and EMG parameters during submaximal isometric contractions monitoring MMG and EMG parameters. Ten male volunteers performed isometric contractions of elbow joint. Triaxial accelerometer-based MMG sensor and EMG electrodes were positioned on the biceps brachii muscle belly. Torque was monitored with a load cell. Volunteers remained seated with hip and elbow joint at angles of 110° and 90°, respectively. The protocol consisted in maintaining torque at 70% of maximum voluntary contraction as long as they could. Parameter data of EMG and the modulus of MMG were determined for four segments of the signal. Statistical analysis consisted of analyses of variance and Fisher’s least square differences post-hoc test. Also, Pearson’s correlation was calculated to determine whether parameters that monitor similar physiological events would have strong correlation. The modulus of MMG mean power frequency (MPF) and the number of crossings in the baseline could detect changes between fresh and fatigued muscle with 1.0 s WLA. MPF and the skewness of the spectrum (μ3), parameters related to the compression of the spectrum, behaved differently when monitored with a triaxial MMG sensor. The EMG results show that for the 1.0 s and 2.0 s WLAs have normalized RMS difference with fatigued muscle and that there was strong correlation between parameters of different domains.
文摘针对上肢肌音信号(Mechanomyography,MMG)动作识别准确率不高的问题,提出一种基于粒子群算法(PSO)与长短期记忆网络相结合的混合模型(Particle Swarm Optimization-Long Short Term Memory,PSO-LSTM)的动作识别方法。采用5通道传感器对受试者进行上肢肌音信号采集,使用巴特沃斯滤波(Butterworth Filter)等方法对肌音信号进行预处理,并进行特征提取;构建基于PSO-LSTM的上肢肌音信号识别模型并进行模型训练和测试;最后从不同测度对比了长短期记忆(LSTM)模型、麻雀搜索算法(Sparrow Search Algorithm,SSA)优化的LSTM模型(Sparrow Search Algorithm-Long Short Term Memory, SSA-LSTM)以及PSO-LSTM模型的实验结果。结果表明,PSO-LSTM模型的准确度均高于LSTM、 SSA-LSTM模型,达到96.9%左右,在迭代损失、迭代速度等方面也优于LSTM、SSA-LSTM模型,从而证明了该模型用于上肢肌音信号识别的优越性。
