Regional cerebral blood flow imaging assessment of brain function reconstruction in elderly hemiplegia patients by body weight support treadmill training

The mechanism underlying body weight support treadmill training in elderly hemiplegic stroke patients is largely unknown. This study aimed to elucidate the changes of cortical blood flow in seven elderly patients with post-stroke hemiplegia before and after body weight support treadmill training by semi-quantitative analysis of regional cerebral blood flow assessed by single photon emission computed tomography. Body weight support treadmill training for 6 months was effective in improving cerebral blood flow and promoting the walking speed and balance recovery in elderly patients with post-stroke hemiplegia.

Experimental Study of an Active Body-Weight Support System for Low Limbs Rehabilitation Training

An experimental study of an active body-weight support（BWS） system for improving treadmill-based locomotion training is performed.The dynamical foundation of the proposed system is developed based on a simplified cable suspended mass-spring-damping system which is used to mimic the vertical gait of a walking human.A specifically designed cable pulley suspended cam-slider system is used to mimic the walking gait of a human in vertical direction.A load cell is installed to connect the slider and the cable which is driven by a winch based on the acceleration feedback.The contact force between the slider and the cam is measured to evaluate the walking load of the system.The experimental results demonstrate that the proposed active BWS system can simultaneously reduce both gravitational and inertial load of the walking body,which implies that the walking body suspended in such a BWS system will dynamically behave as if certain amount of body mass had been removed.

Coordinated control strategy for robotic-assisted gait training with partial body weight support

Walking is the most basic and essential part of the activities of daily living. To enable the elderly and non-ambulatory gait-impaired patients, the repetitive practice of this task, a novel gait training robot(GTR) was designed followed the end-effector principle, and an active partial body weight support(PBWS) system was introduced to facilitate successful gait training. For successful establishment of a walking gait on the GTR with PBWS, the motion laws of the GTR were planned to enable the phase distribution relationships of the cycle step, and the center of gravity(COG) trajectory of the human body during gait training on the GTR was measured. A coordinated control strategy was proposed based on the impedance control principle. A robotic prototype was developed as a platform for evaluating the design concepts and control strategies. Preliminary gait training with a healthy subject was implemented by the robotic-assisted gait training system and the experimental results are encouraging.

Multi-mode process monitoring based on a novel weighted local standardization strategy and support vector data description

There are multiple operating modes in the real industrial process, and the collected data follow the complex multimodal distribution, so most traditional process monitoring methods are no longer applicable because their presumptions are that sampled-data should obey the single Gaussian distribution or non-Gaussian distribution. In order to solve these problems, a novel weighted local standardization(WLS) strategy is proposed to standardize the multimodal data, which can eliminate the multi-mode characteristics of the collected data, and normalize them into unimodal data distribution. After detailed analysis of the raised data preprocessing strategy, a new algorithm using WLS strategy with support vector data description(SVDD) is put forward to apply for multi-mode monitoring process. Unlike the strategy of building multiple local models, the developed method only contains a model without the prior knowledge of multi-mode process. To demonstrate the proposed method's validity, it is applied to a numerical example and a Tennessee Eastman(TE) process. Finally, the simulation results show that the WLS strategy is very effective to standardize multimodal data, and the WLS-SVDD monitoring method has great advantages over the traditional SVDD and PCA combined with a local standardization strategy(LNS-PCA) in multi-mode process monitoring.

Weight Data Fusion Based on Mutual Support Applied in Large Diameter Measurement

The on-line diameter measurement of larger axis workpieces is hard to achieve high precision detection, because of the bad environment of locale, the problem to amend the measuring error by non-uniform temperature field, and the difficulty to collimate and locate by usual method. By improving the measurement accuracy of larger axis accessories, it is useful to raise axis and hole＇s industry produce level. Because of the influence of complex environment in locale and some influential factors which are hard excluded from the large diameter measurement with multi-rolling-wheels method, the measurement results may not support or even contradict each other. To the situation, this paper puts forward a mutual support deviation distinguish data fusion method, including mutual support deviation detection and weight data fusion. The mutual support deviation detection part can effectively remove or weaken the unexpected impact on the measurement results and the weight data fusion part can get more accurate estimate result to the detected data. So the method can further improve the reliability of measurement results and increase the accuracy of the measurement system. By using the weight data fusion based on the mutual support （DFMS） to the simulation and experiment data, both simulation results and experiment results show that the method can effectively distinguish the data influenced by unexpected impact and improve the stability and reliability of measurement results. The new provided mutual support deviation distinguish method can be used to single sensor measurement and multi-sensor measurement, and can be used as a reference in the data distinguish of other area. The DFMS is helpful to realize the diameter measurement expanded uncertainty in 5 ×10^-6D or even higher when the measured axis workpiece＇s diameter is 1-5 m （ 1 m ≤ D ≤5 m ）.

ON THE WEIGHTED OCCUPATION TIME AND SUPPORT OF SUPER-BROWNIAN MOTION CONDITIONED ON NON-EXTINCTION

This paper investigates the property of super-Brownian motion conditioned on non-extinction. The authors obtain a representation of Laplace functional for the weighted occupation time of this class of processes. By this, they get a result about the distribution of the support of it.

Fault Diagnosis Model Based on Fuzzy Support Vector Machine Combined with Weighted Fuzzy Clustering

A fault diagnosis model is proposed based on fuzzy support vector machine (FSVM) combined with fuzzy clustering (FC).Considering the relationship between the sample point and non-self class,FC algorithm is applied to generate fuzzy memberships.In the algorithm,sample weights based on a distribution density function of data point and genetic algorithm (GA) are introduced to enhance the performance of FC.Then a multi-class FSVM with radial basis function kernel is established according to directed acyclic graph algorithm,the penalty factor and kernel parameter of which are optimized by GA.Finally,the model is executed for multi-class fault diagnosis of rolling element bearings.The results show that the presented model achieves high performances both in identifying fault types and fault degrees.The performance comparisons of the presented model with SVM and distance-based FSVM for noisy case demonstrate the capacity of dealing with noise and generalization.

基于特征判定系数的电力变压器振动信号故障诊断

变压器带电故障诊断对于保证电力变压器安全平稳运行具有重要的意义。针对变压器工作环境复杂且单一参数表征变压器故障类型不全面的问题,文中提出一种基于自适应噪声完备集合经验模态分解(complete ensemble empirical mode decomposition with adaptive noise,CEEMDAN)和特征熵权法(entropy weight method,EWM)进行故障诊断的方法。通过相关系数与峭度加权(correlation coefficient and weighted kurtosis,CCWK)原则筛选CEEMDAN分量并重构信号,在实现剔除冗余分量的同时,提升变压器振动信号特征的表征能力;利用EWM构建特征判定系数实现单一数据诊断变压器故障类型;通过主成分分析法减小混合域特征尺度,采用鸡群优化算法优化支持向量机(support vector machine,SVM)模型进行故障诊断。对某变电站110 kV三相油浸式变压器进行分析,结果表明与概率神经网络和SVM等变压器故障诊断方法相比,文中方法能在提前定性故障类型的同时,进一步提高变压器故障诊断的准确率与效率。

全髋关节置换后减重太极步法可改善平衡功能

背景:全髋关节置换后患者大多存在平衡障碍,需要应用多种康复方法来改善,减重太极步法可以作为一种安全有效的平衡训练方法。目的:观察减重太极步法对全髋关节置换后患者平衡功能的影响。方法:招募74例全髋关节置换后患者,随机分为对照组和试验组(n=37)。对照组实施减重步行训练30 min和常规康复训练60 min;试验组实施减重太极步法训练30 min和常规康复训练60 min;两组均每天1次,一周5次,持续12周。于干预前、干预4,8,12周后分别采用Berg平衡量表和动态平衡能力测试来评估平衡功能,Harris评分评估髋关节功能,跌倒风险指数评估跌倒风险。结果与结论:①4个观察指标均显示有显著的时间效应(P<0.001);②Berg平衡量表、Harris评分与跌倒风险指数均具有交互作用(P<0.001),并在干预12周后具有显著的组间差异(P<0.001),且试验组效果优于对照组;③干预12周后动态平衡能力测试的前和左方向评分有交互作用和组别效应(P<0.001),整体、前、左、右方向评分具有显著组别差异(P<0.001);④结果表明,干预12周后,试验组与对照组的平衡功能均有改善,且减重太极步法训练对全髋关节置换后患者的改善效果优于减重步行训练。

适应人体重心起伏的悬吊减重康复系统设计

减重系统对下肢疾病患者的行走康复训练有重要影响。现有的下肢康复外骨骼减重装置大多只考虑如何减去患者体重的百分比,而忽略了患者身体重心的起伏。由于外骨骼的骨盆支架在竖直方向运动轨迹固定,患者步态的微小变化就会导致重心高度与骨盆支架运动轨迹不符,这一差异会作用到患者的骨盆位置,影响下肢关节的活动,并产生额外的风险。针对这一问题,提出通过采集足底压力来预测重心位置变化,并利用获得的重心轨迹解算出应施加的减重力方法,为患者训练提供安全有效的减重系统。所提方法的可行性已获仿真证实,并在研发的外骨骼减重系统上获得实际验证,模糊控制器跟踪重心轨迹的误差相比PID控制减少了21.2%,稳态误差维持在±1 mm范围内,髋膝关节的活动范围相比常规减重分别增加了14.36%和13.77%。

基于特征加权混合隶属度的模糊孪生支持向量机

模糊孪生支持向量机(FTSVM)忽略了不同特征间的差异,导致核函数或距离的计算无法准确反映样本间的相似性,使FTSVM在处理含有大量不相关或弱相关特征的高维数据分类时,难以达到良好分类效果;且隶属度的设计未有效区分离群点或噪声。针对以上问题,提出了一种基于特征加权混合隶属度的FM-FTSVM。首先计算每个特征的信息增益,并依据信息增益值的大小为特征赋予权重,降低不相关或弱相关特征的作用,使其能更好地应用于高维数据分类;然后,为每一类样本构造一个最小包围球计算基于紧密度的特征加权隶属度,并结合基于距离的特征加权隶属度得到特征加权混合隶属度,综合考虑样本点到类中心的特征加权欧式距离和样本间的紧密程度,可更好识别离群点或噪声数据;最后,融合特征加权核函数,降低不相关特征对核函数或距离计算产生的影响。与对比算法在人工数据集、高维数据集和UCI数据集上进行比较,发现本文提出的方法在区分离群点、噪声和有效样本上有明显优势,且在高维数据集上可获得更好分类效果。

减重结合功能性电刺激对脑卒中患者下肢运动功能的疗效

目的观察减重结合功能性电刺激对脑卒中患者下肢运动功能的疗效。方法2023年1月至2024年2月,选择佛山市禅城区人民医院康复医院住院脑卒中患者60例,随机分为对照组(n=30)和观察组(n=30)。对照组接受常规康复,观察组增加天轨系统减重结合功能性电刺激,共3周。治疗前后采用平衡评定与训练仪、Berg平衡量表(BBS)、FMA评定量表下肢部分(FMA-LE)、Holden步行功能分级(FAC)进行评定。结果治疗后,两组X轴轨迹长、动摇平均速度、动摇总轨迹长、Y轴轨迹长、单位面积轨迹长、BBS评分、FMA-LE评分、FAC评分均较治疗前显著改善(|t|>4.809,P<0.001)。除X轴轨迹长和动摇平均速度外,其他指标观察组均优于对照组(|t|>2.268,P<0.05)。结论增加减重结合功能性电刺激能更有效改善脑卒中患者的下肢运动功能、平衡功能和步行功能。

MOTOmed智能训练系统联合减重步态训练改善脑卒中偏瘫患者偏瘫步态研究

目的观察MOTOmed智能训练系统联合减重步态训练对脑卒中偏瘫患者伸肌协同偏瘫步态的运动学影响。方法收集2021年12月—2022年12月在沧州市人民医院住院治疗的脑卒中偏瘫患者共90例,并按照随机数字表法将其分为MOTOmed智能训练系统组(对照组)以及MOTOmed智能训练系统联合减重步态训练组(试验组),每组45例。2组均采用常规康复治疗,辅以MOTOmed智能系统训练,包括利用MOTOmed智能系统的下肢环转运动训练和主动模式进行抗阻力训练。试验组增加减重步态训练,减小重量从体质量的30%开始,根据患者身体情况酌量减少至体质量15%水平,或完全负重,平板运行速度为0.2~1.0 m/s。2组在治疗前及治疗4周后进行康复评估,应用Barthel指数评定量表、Holden步行功能评定、偏瘫步态分析评价量表、下肢运动功能评估(FMA-LE)及平衡功能评估(BBS),比较2组的康复治疗效果。结果与组内治疗前比较,2组治疗4周后Barthel指数评定量表评分、Holden步行功能分级、偏瘫步态分析均有所改善(P<0.05)。与对照组相比较,试验组治疗4周后Barthel指数评定提高(P<0.05),患侧支撑期百分比、步速、步频和步长增大(P>0.05),双支撑期百分比减少(P<0.05),Holden步行功能分级改善(P<0.05),FMA-LE和BBS评分增加(P<0.05)。结论MOTOmed智能训练系统联合减重步态训练可有效地改善脑卒中伸肌协同偏瘫患者的步态及步行的能力。

厚煤层大采高综采面顶板破断规律及机理研究

以杭来湾煤矿首个大采高30201工作面为例,通过数值模拟、理论分析和现场实测的方法,分别对工作面开采过程中顶板破断规律进行研究。基本顶以“悬臂梁”、“砌体梁”的结构形式周期发生破断,造成工作面大、小周期来压显现。建立了工作面顶板力学模型,给出来压步距的计算方法。现场实测表明最大步距67.6 m是平均步距34.4 m的1.97倍,说明约2次小周期来压才有1次大周期来压。

基于ICEEMDAN和时变权重集成预测模型的变压器油中溶解气体含量预测

为了实现对变压器油中溶解气体体积分数的精确预测,同时克服仅使用单一预测模型导致预测精度及泛化能力不足的局限,提出了一种基于改进完全自适应噪声集合经验模态分解(improved complete ensemble empirical mode decomposition,ICEEMDAN)和灰色关联系数时变权重集成预测模型的变压器油中溶解气体预测方法。首先将溶解气体含量序列模态分解为一系列具有不同时间尺度的子序列。然后,使用门控循环神经网络和麻雀搜索算法优化支持向量机对各子序列进行训练,组合为一个集成预测模型;并比较不同预测方法的预测精度,计算灰色关联系数时变权重,形成各子系列的预测结果。最后将各子序列的预测结果叠加重构,得到最终预测结果。算例分析结果显示:该方法单步预测的均方根误差、平均绝对误差和相关系数分别为0.593、0.422和0.768,相比其他算法在预测精度上有明显提升,同时具有很强的泛化性能,可以为油浸式变压器内部状态监测提供依据。

大中型水库移民生产生活水平指标体系与实证评价研究

以GX自治区WZ市2021年度大中型水库移民后期扶持政策实施情况监测评估项目为案例,基于10个移民村200户移民家庭的原始调查数据,采用灰色聚类-复相关-信息灵敏度法,构建了包括人均可支配收入等指标的评价指标体系;利用熵值法和环比评分法分别计算指标权重后,应用基于博弈论的组合赋权法确定了评价指标的主客观组合权重;结合调查数据及官方统计数据确定指标分级标准,利用综合评分法计算移民的生产生活水平指数,并运用该指数对样本村水库移民生产生活水平进行了实证评价。结果表明,采用本文方法构建的指标体系和评价模型相对合理、有效,评价结果符合实际,可为水库移民后期扶持效果评价及其他相关评价工作提供参考。

杭来湾煤矿30201综采面矿压显现及支架适应性分析

综采面顶板控制是矿井安全生产中面临的关键问题,有必要对综采面矿压显现规律和支架适应性开展研究,以保障综采面安全生产。通过对杭来湾煤矿30201工作面矿压显现规律进行现场观测和分析,发现工作面支架工作阻力整体表现为“中间大、两头小”的分布规律;上、中、下部支架平均工作阻力分别约为31.76 MPa、37.91 MPa和45.44 MPa,平均动载系数分别为1.228、1.413、1.291,支架安全阀平均开启次数分别为7.8、29.5、8.6;建立了工作面正常开采期间与周期来压期间支架-顶板力学模型,给出了相应的支架临界工作阻力计算方法,计算得到正常开采期间支架所需载荷为14700 kN,周期来压期间所需载荷为17050 kN。经验证,30201工作面ZY21000/36/75D号支架能够满足安全生产要求,研究成果可为后续矿井开采设计、顶板控制等提供借鉴。

基于加权SVM的电力通信网络异常流量入侵检测方法

当前检测方法一般为目标执行检测,检测覆盖范围容易受限,导致误报率增加,为此提出基于加权支持向量机(Support Vector Machine,SVM)的电力通信网络异常流量入侵检测方法研究。先提取异常流量隐性特征,采用动态化的方式打破检测覆盖范围受到的限制。设计动态异常流量检测机制,并以此为基础构建基于加权SVM的通信网络异常流量入侵检测模型,采用交叉跟踪识别的方式来完成入侵检测。测试结果表明,应用所提方法检测时,其误报率控制在15%以下,证明设计方法具有较强的稳定性与针对性,实际应用效果更佳。

一般自我效能感在孕妇体重控制中的作用分析

目的探索影响孕妇孕前体重及孕期体重增加(GWG)的相关因素,了解孕妇自身一般自我效能对于其体重控制的直接影响与中介效应。方法于2019年1月至2020年7月对在某社区卫生服务中心建档的900名孕妇进行问卷调查。结果AMOS中介效应检验结果显示,孕妇的孕前个人社会基础、孕前社会心理支持、孕前一般效能感、医生孕期体重管理认可度对孕妇体重控制有直接正向中介效应,而一般自我效能还在社会心理支持水平与GWG和孕前BMI之间起部分中介效应;另外,孕前BMI、孕妇对医生孕期体重管理认可度则在一般自我效能与GWG结果之间起部分中介效应。结论应对低一般自我效能感和低社会心理支持水平孕妇的体重控制情况予以重点关注,制定个性化的干预措施并开展综合干预。

基于HHO-SVM的抗SSDF攻击协作频谱感知方法

针对认知无线电网络中的频谱感知数据伪造(spectrum sensing data falsification,SSDF)攻击问题,提出一种基于哈里斯鹰优化(Harris hawks optimization,HHO)算法和支持向量机(support vector machine,SVM)的抗SSDF攻击协作频谱感知方法。首先从报告信息矩阵中提取用于区分次用户(secondary users,SU)类别的特征向量。其次通过HHO算法优化SVM内核参数,通过优化的SVM模型检测恶意SU,提高了在复杂感知环境中对SU分类的准确率。最后根据优化的SVM模型计算获得SU的可信度,并以可信度为权重融合感知数据,进一步加强系统的抗攻击性。仿真结果表明,所提方法能够对不同的SSDF攻击场景实现有效防御,相比现有的方法具有更好的频谱感知性能。