To address the problems in the application of lower limb exoskeleton mechanical equipment, XGBOOT Algorithm-based research on gait phase recognition is carried out, only using motion attitude data measured by a single IMU. Firstly, foot motion data of six different gaits are collected, and each gait is divided into four phases. On this basis, XGBOOT algorithm optimized is applied to analyze the gait phase recognition with the foot motion data as the training set. In the process of establishing the model, the parameters involved in the model are further optimized by the Bayesian optimization algorithm（BOA）. Through calculation, the results show that the average accuracy of the model is 89.26% in the verification set, the precision of the model is 89.64% in the verification set, the recall rate of the model is 89.26% in the verification set, F1 value of the model is 89.10% in the verification set, which indicates that the model can achieve better gait phase recognition.

Aiming at the problems of individual differences in the asynchrony process of human lower limbs and random changes in stride during walking, this paper proposes a method for gait recognition and prediction using motion posture signals. The research adopts an optimized gated recurrent unit (GRU) network algorithm based on immune particle swarm optimization (IPSO) to establish a network model that takes human body posture change data as the input, and the posture change data and accuracy of the next stage as the output, to realize the prediction of human body posture changes. This paper first clearly outlines the process of IPSO's optimization of the GRU algorithm. It collects human body posture change data of multiple subjects performing flat-land walking, squatting, and sitting leg flexion and extension movements. Then, through comparative analysis of IPSO optimized recurrent neural network (RNN), long short-term memory (LSTM) network, GRU network classification and prediction, the effectiveness of the built model is verified. The test results show that the optimized algorithm can better predict the changes in human posture. Among them, the root mean square error (RMSE) of flat-land walking and squatting can reach the accuracy of 10 -3, and the RMSE of sitting leg flexion and extension can reach the accuracy of 10 -2. The R 2 value of various actions can reach above 0.966. The above research results show that the optimized algorithm can be applied to realize human gait movement evaluation and gait trend prediction in rehabilitation treatment, as well as in the design of artificial limbs and lower limb rehabilitation equipment, which provide a reference for future research to improve patients' limb function, activity level, and life independence ability.

通过向碱度为1的球团矿中配加不同比例赤铁矿,探究赤铁矿的加入对碱性球团性能的影响.试验结果表明,随着赤铁矿的配比由0％提高到40％,生球落下强度提高、抗压强度变化不明显;焙烧球团矿的抗压强度下降,由未加赤铁矿的2 460 N/P降低到含40％赤铁矿的1 890 N/P;成品球内部孔隙率由25.84％下降到21.45％,平均孔径由2 184.7提高到3 937.9 nm;通过显微观察,发现赤铁矿再结晶比磁铁矿固相固结方式形成的晶体结构松散,晶体间的联结较弱;配加赤铁矿对碱性球团矿还原膨胀率起到明显改善作用,由30.45％降低到13.23％.综合来看,向碱度为1的球团矿中配加适量赤铁矿的技术是可行的.

针对下肢术后康复训练需求,设计并开发了一种基于嵌入式控制器和远程控制架构的下肢康复机器人控制与监测系统,实现了下肢康复机器人的主动/被动训练模式控制、运动姿态与肌电信号采集、WIFI通讯、安全保护等功能,通过应用随机森林机器学习算法和线性回归算法实现了训练过程的识别与分析.实验结果表明:所研制的下肢康复机器人控制与监测系统可以通过安卓进行便携控制,并能通过训练过程中的监测信号,实现对训练过程的智能分析.同时可知,随机森林算法相对线性回归算法在运动识别方面更有优势,这对训练过程的自动监测和智能化控制具有积极意义.

为了优化高炉炉料结构,增加镁质熔剂性球团矿人炉配比(球比),对不同碱度(R)镁质熔剂性球团冶金性能和高炉炉料结构熔滴性能进行研究.结果 表明:白云石的粘结作用,使得镁质熔剂性球团生球性能明显改善;随着R增大,焙烧球中Mg2+进入磁铁矿晶格中并占据了铁离子扩散产生的空位,并生成铁酸镁和钙镁橄榄石,降低了Fe2O3再结晶固结能力,使球团的强度降低,球团还原过程中Mg2+能均匀分布在浮氏体内,使得还原膨胀率逐渐降低;还原度指数在R=0.56时降低明显,但随着R增大还原度指数明显升高,有利于还原;单一镁质熔剂性球团的软化熔滴性都有不同程度的改善,R=1.2时球团性能最好;高球比综合炉料软化熔滴性均有明显改善,炉料结构为25％烧结矿+75％球团矿时效果最好.综合考虑,球团R不宜过高或过低,应控制在1.0左右.

为降低膨润土用量,提高入炉球团矿品位,本文对玉米芯进行碱化处理,探究其作为有机黏结剂应用到球团生产中的可行性,并研究其黏结机理.结果表明:随着碱化剂的NaOH质量分数、碱化时间、固液比的改变,玉米芯发生不同程度的水解,最佳预处理条件下NaOH的质量分数为6％、碱化时间为4 h;2％玉米芯配加1％膨润土作为黏结剂时,生球的抗压强度达到11.5 N/P,落下强度达到10.1次/(0.5 m);玉米芯黏结剂与铁精矿粉之间由于存在配位键、氢键等吸附作用,同时玉米芯的复杂纤维空间结构可网罗大量的铁精粉颗粒,可使球团内部结构更加紧密.