Exploring the effect of fingertip aero-haptic feedforward cues in directing eyes-free target acquisition in VR

Background The sense of touch plays a crucial role in interactive behavior within virtual spaces,particularly when visual attention is absent.Although haptic feedback has been widely used to compensate for the lack of visual cues,the use of tactile information as a predictive feedforward cue to guide hand movements remains unexplored and lacks theoretical understanding.Methods This study introduces a fingertip aero-haptic rendering method to investigate its effectiveness in directing hand movements during eyes-free spatial interactions.The wearable device incorporates a multichannel micro-airflow chamber to deliver adjustable tactile effects on the fingertips.Results The first study verified that tactile directional feedforward cues significantly improve user capabilities in eyes-free target acquisition and that users rely heavily on haptic indications rather than spatial memory to control their hands.A subsequent study examined the impact of enriched tactile feedforward cues on assisting users in determining precise target positions during eyes-free interactions,and assessed the required learning efforts.Conclusions The haptic feedforward effect holds great practical promise in eyeless design for virtual reality.We aim to integrate cognitive models and tactile feedforward cues in the future,and apply richer tactile feedforward information to alleviate users'perceptual deficiencies.

Improving the spaceborne GNSS-R altimetric precision based on the novel multilayer feedforward neural network weighted joint prediction model

Global navigation satellite system-reflection(GNSS-R)sea surface altimetry based on satellite constellation platforms has become a new research direction and inevitable trend,which can meet the altimetric precision at the global scale required for underwater navigation.At present,there are still research gaps for GNSS-R altimetry under this mode,and its altimetric capability cannot be specifically assessed.Therefore,GNSS-R satellite constellations that meet the global altimetry needs to be designed.Meanwhile,the matching precision prediction model needs to be established to quantitatively predict the GNSS-R constellation altimetric capability.Firstly,the GNSS-R constellations altimetric precision under different configuration parameters is calculated,and the mechanism of the influence of orbital altitude,orbital inclination,number of satellites and simulation period on the precision is analyzed,and a new multilayer feedforward neural network weighted joint prediction model is established.Secondly,the fit of the prediction model is verified and the performance capability of the model is tested by calculating the R2 value of the model as 0.9972 and the root mean square error(RMSE)as 0.0022,which indicates that the prediction capability of the model is excellent.Finally,using the novel multilayer feedforward neural network weighted joint prediction model,and considering the research results and realistic costs,it is proposed that when the constellation is set to an orbital altitude of 500 km,orbital inclination of 75and the number of satellites is 6,the altimetry precision can reach 0.0732 m within one year simulation period,which can meet the requirements of underwater navigation precision,and thus can provide a reference basis for subsequent research on spaceborne GNSS-R sea surface altimetry.

Research on Anti-Fluctuation Control of Winding Tension System Based on Feedforward Compensation

In the fiber winding process,strong disturbance,uncertainty,strong coupling,and fiber friction complicate the winding constant tension control.In order to effectively reduce the influence of these problems on the tension output,this paper proposed a tension fluctuation rejection strategy based on feedforward compensation.In addition to the bias harmonic curve of the unknown state,the tension fluctuation also contains the influence of bounded noise.A tension fluctuation observer(TFO)is designed to cancel the uncertain periodic signal,in which the frequency generator is used to estimate the critical parameter information.Then,the fluctuation signal is reconstructed by a third-order auxiliary filter.The estimated signal feedforward compensates for the actual tension fluctuation.Furthermore,a time-varying parameters fractional-order PID controller(TPFOPID)is realized to attenuate the bounded noise in the fluctuation.Finally,TPFOPID is enhanced by TFO and applied to control a tension control system considering multi-source disturbances.The stability of the method is analyzed by using the Lyapunov theorem.Finally,numerical simulations verify that the proposed scheme improves the tracking ability and robustness of the system in response to tension fluctuations.

Laboratory Implementation of Direct Torque Controller based Speed Loop Pseudo Derivative Feedforward Controller for PMSM Drive

This paper,evaluate the effectiveness of a proposed speed loop pseudo derivative feedforward(PDFF)controller-based direct torque controller(DTC)for a PMSM drive against the performance of existing PI speed controller-based DTC and hysteresis current controller(HCC).The proposed PDFF-based speed regulator effectively reduces oscillation and overshoot associated with rotor angular speed,electromagnetic torque,and stator current.Two case studies,one using forward-to-reverse motoring operation and the other involving reverse-to-forward braking operation,has been validated to show the effectiveness of the proposed control strategy.The proposed controller's superior performance is demonstrated through experimental verification utilizing an FPGA controller for a 1.5 kW PMSM drive laboratory prototype.

Application Effect of Feedforward Control in Outpatient Blood Specimen Management

Objective:To analyze the application effect of feedforward control in outpatient blood specimen management.Methods:1,200 patients who had their venous blood collected in outpatient phlebotomy room of our hospital's outpatient clinic from January 2021 to April 2021 were selected as study subjects and divided into 600 cases in the control group and 600 cases in the observation group.The two groups of patients were compared in terms of their satisfaction with the staff,the efficiency of the nurses and the quality of nursing care,turnaround time before specimen analysis,the rejection rate of the blood specimens,and the time of result reporting.Results:After the implementation of feedforward control,patients'satisfaction with staff,nurses work efficiency and quality of care,turnaround time before specimen analysis,specimen rejection rate,and result reporting time in the observation group were significantly higher than those in the control group(P<0.05).Conclusion:The application of feedforward control in the management of outpatient blood specimens has significant effect,which effectively improves patients'satisfaction,enhances the efficiency of nurses and the quality of nursing care,shortens the turnaround time of specimens before analysis and the reporting time of results,and reduces the rejection rate of specimens.

基于前馈补偿的轮腿式平台多任务复合运动解耦控制

轮腿式平台在执行复杂作战环境载运任务时,由于其动态协调腿部与车轮运动是复杂耦合的,其行驶速度的稳定性、位姿控制的精确性都十分困难.文中基于轮腿式平台通过有限高度场景的动态协调任务,提出了一种基于前馈补偿的多任务优先级复合运动解耦控制框架.首先,分别建立了关节和车轮动力学模型,降低模型复杂度;然后,搭建了基于任务优先级的前馈关节力矩解算,叠加轮端前馈交互力和反馈控制力矩降低实时位姿调整对质心速度的影响;最后,采用转速闭环PI控制器,并引入车轮滚动阻力实现车轮速度到质心速度的有效传递.仿真结果表明,所提出控制方法在受限场景运动过程中平台质心速度最大跟踪误差小于3%,为轮腿式平台复杂环境下执行任务提供了可行控制框架.

Self Adjusting Feedforward Compensation Tracking Control for Proportional Valve Controlled Motor

Aim Aiming at the position tracking control for valve controlled motor electrohydraulic proportional servo systems mainly driving the static load torque, the tracking performance was studied in the presence of the variable gain and deadzone. Methods On the basis of conventional composite control with the deadzone compensation method, a comprehensive control approach with the deadzone and self adjusting feedforward compensation was proposed. Results Experimental results showed that the good tracking performance was achieved for the sinusoidal and constant velocity position tracking under a wide variations of load torque. Conclusion The position tracking accuracy for valve controlled motor electrohydraulic proportional servo systems has been solved by using the comprehensive control approach with the deadzone and self adjusting feedforward compensation.

弱电网下多逆变器并网阻抗重塑谐振抑制策略

弱电网下多逆变器并网系统可能同时受到较大电网阻抗及背景谐波影响,电网阻抗较大导致逆变器与电网交互作用增强从而产生谐振问题,背景谐波进一步导致并网电压及电流波形畸变,使系统无法满足并网要求。鉴于此,文中建立弱电网下多逆变器并网系统阻抗模型,明确弱电网下并网电流波形畸变及谐振机理,进而提出一种适用于逆变器的改进电网电压前馈与并联自适应有源阻尼器结合的控制策略。利用改进电网电压前馈重塑多逆变器并网系统阻抗从而抑制背景谐波电压畸变的影响,利用有源阻尼器合成虚拟电阻以抑制系统与弱电网间的谐振,当系统工况变化时自适应调节虚拟电阻值并通过虚拟电阻补偿环节进一步改进阻尼效果。仿真结果表明所提策略能有效抑制背景谐波及并网系统谐振,提高多逆变器并网系统弱电网情况下的适应性。

伺服作动系统性能测试平台的多余力抑制方法

为了解决结构不变性补偿方法抑制多余力的实时调参问题,建立了伺服作动系统性能测试平台的数学模型和仿真模型,提出了速度前馈补偿器的解耦控制方法,设计了模糊控制器以抑制多余力。对比了传统比例-积分(proportional-integral, PI)控制、传统PI前馈补偿控制和模糊PI前馈补偿控制三种控制方法对测试平台多余力的抑制效果。仿真结果表明,模糊PI前馈补偿控制方法可以提高测试平台的加载精度,对多余力的抑制效果最为显著。通过试验与仿真的对比,验证了该控制方法的有效性,为伺服作动系统测试平台实现大推力、高精度加载提供理论参考。

LEARNING ALGORITHM OF FEEDFORWARD NEURAL NETWORK WITH HARD LIMITER USED FOR CLASSIFICATION

A learning algorithm based on a hard limiter for feedforward neural networks (NN) is presented,and is applied in solving classification problems on separable convex sets and disjoint sets.It has been proved that the algorithm has stronger classification ability than that of the back propagation (BP) algorithm for the feedforward NN using sigmoid function by simulation.What is more,the models can be implemented with lower cost hardware than that of the BP NN.LEARNIN

考虑全前馈的重复控制逆变器稳定性提升策略

电网电压全前馈可以提高重复控制并网逆变器的动态性能,同时抑制电网电压背景谐波对并网电流的影响。然而全前馈环节的引入会使重复控制并网逆变器在弱电网下的稳定性变差。本文研究了采用电网电压全前馈的重复控制并网逆变器在弱电网下稳定性提升策略,首先建立了包含电网阻抗和电网电压前馈的等效数字控制对象,通过小增益定理稳定性分析方法可知,降低系统无重复控制时的闭环传递函数增益有利于系统稳定。对此,本文提出了在全前馈环节中加入一阶低通滤波器,提高了系统的稳定性。谐波抑制效果分析表明,改进全前馈为系统提供了良好的谐波抑制能力。搭建了一台20kW逆变器样机并进行实验,结果表明该策略可以有效提高系统稳定性。

Generalization Capabilities of Feedforward Neural Networks for Pattern Recognition

This paper studies the generalization capability of feedforward neural networks (FNN).The mechanism of FNNs for classification is investigated from the geometric and probabilistic viewpoints. It is pointed out that the outputs of the output layer in the FNNs for classification correspond to the estimates of posteriori probability of the input pattern samples with desired outputs 1 or 0. The theorem for the generalized kernel function in the radial basis function networks (RBFN) is given. For an 2-layer perceptron network (2-LPN). an idea of using extended samples to improve generalization capability is proposed. Finally. the experimental results of radar target classification are given to verify the generaliztion capability of the RBFNs.

基于干涉和实时前馈实现三个量子节点的纠缠

纠缠多个空间分离的远程量子节点是构建量子信息网络的基础,不仅如此,多量子节点间的纠缠亦可应用于分布式量子计算和量子精密测量等,实现更高纠缠度的多量子节点间纠缠势在必行.文中基于干涉和实时前馈,借助于腔增强通过采用受激拉曼散射机制实现三个远距离原子系综的纠缠.该方案中只需要三套腔增强原子系统,光学谐振腔增强了光和原子之间的相互作用,使得通过受激拉曼散射产生的斯托克斯光场和反斯托克斯光场的效率得到提高,实现了高纠缠度的光与原子纠缠.在此前提下,基于斯托克斯光场的干涉和实时前馈实现三个远程原子节点间的纠缠.这种方法可以扩展到更大规模,甚至更加复杂的量子信息网络中,从而在量子信息科学中具有潜在的应用.

燃料电池氢气系统自适应滑模解耦控制研究

有效的燃料电池氢气系统控制策略可以改善系统动态性能并延长使用寿命,本文针对循环泵型燃料电池氢气系统,提出了基于梯度寻优的自适应滑模解耦控制策略。首先基于Simulink搭建了燃料电池氢气系统模型,基于该模型设计了解耦滑模控制器在补偿模型精度不准的同时实现了流量和压力的解耦,并通过李雅普诺夫原理证明了反馈控制率的稳定性。然而,滑模控制存在动态响应性能和抖振相矛盾的问题,对此,本研究进一步设计了基于梯度下降的滑模控制参数自适应寻优方法,并通过前馈控制器提高变载时的系统稳定性,同时采用滑模调优参数Map自学习的方式,在保证闭环系统稳定的同时解决了瞬态工况下的梯度寻优延迟问题。结果表明:本文设计的结合前馈的自适应滑模解耦控制器超调量较小,响应时间较短,鲁棒性较高;阴阳极压差最大值约为0.01 bar,流量供给误差最大为0.015 g/s;能够在0.02 s内迅速响应变载运行时氢气压力和流量的变化需求;相比于前馈修正之前,电堆启动工况下的压力波动降低了0.122 bar;扰动作用下,系统稳定性保持良好,氢气压力波动最大为0.01 bar。

基于神经认知科学的卫衣情感分析与建模

在消费升级与情感体验日益重视的时代背景下,服装的情感化设计成为制约消费者是否购买产品的关键。为了探索服装设计元素与消费者情感反应的关系,本文以卫衣作为研究载体,借助PAD情绪模型、眼动追踪和脑电波(EEG)技术,系统分析消费者对服装元素(款式、色彩、图案)的情感反应。结果表明,色彩搭配中单色和双色组合更易激发积极情绪,而三色较易引起消极情绪;图案大小和位置设计更易产生积极情绪;款式元素引起的情绪变化无明显规律;同时,以客观生理指标为输入,以主观情感指标为输出,构建服装情感识别模型;最后通过损失曲线、混淆矩阵、ROC曲线及误差的分析对情感识别模型进行了评估。研究表明,本文构建的服装情感识别模型准确率为98%,能高效准确识别服装情感类别,可以为更契合消费者情感需求的卫衣情感化定制提供参考。

IDENTIFICATION OF NONLINEAR TIME VARYING SYSTEM USING FEEDFORWARD NEURAL NETWORKS

As it is well known,it is difficult to identify a nonlinear time varying system using traditional identification approaches,especially under unknown nonlinear function.Neural networks have recently emerged as a successful tool in the area of identification and control of time invariant nonlinear systems.However,it is still difficult to apply them to complicated time varying system identification.In this paper we present a learning algorithm for identification of the nonlinear time varying system using feedforward neural networks.The main idea of this approach is that we regard the weights of the network as a state of a time varying system,then use a Kalman filter to estimate the state.Thus the network implements nonlinear and time varying mapping.We derived both the global and local learning algorithms.Simulation results demonstrate the effectiveness of this approach.

基于多模态特征融合的Android恶意程序检测方法研究

现有Android恶意程序检测方法主要使用单模态数据来表征程序特征,未能将不同的特征信息进行充分挖掘和融合,导致检测效果不够理想。为了提升检测的准确率和鲁棒性,提出一种基于多模态特征融合的Android恶意程序检测方法。首先对权限信息进行编码处理并将Dalvik字节码数据可视化为“矢量”RGB图像,然后构建前馈神经网络和卷积神经网络分别对文本和图像模态表征的数据进行特征提取,最后对提取的不同模态特征向量分配不同的权重并相加进行融合后对其进行分类。实验结果表明,该方法对Android恶意程序的识别准确率和F1分数都达到了98.66%,且具有良好的鲁棒性。

基于个性化联邦学习的异构船舶航行油耗预测

船舶航行油耗的精准预测,对保护海洋环境、减少航运业运营成本起关键作用,但航运业船舶的数据私密性、及异构船舶的数据异质性,导致常规机器学习方法的预测效果有限。为此,提出一种基于类别型特征的梯度提升(CatBoost)联合个性化联邦学习(PFL)预测方法。首先,对本地不同数据源的船舶信息数据及海况数据进行数据融合和清洗过滤,以提高输入数据质量;其次,对本地融合数据用CatBoost进行特征选取,以去除冗余数据;随后,引入带个性化层的联邦学习(FedPer)框架,建立异构船舶航行油耗预测模型,以保证异构船舶的数据私密性;进一步,对基本层权重矩阵采用联邦平均算法(FedAvg)聚合参数并反馈,对个性化层权重矩阵由本地客户端采用深度前馈神经网络(DFNN)进行训练优化,以消除数据异质性的影响,提高预测精度。最后,结合实际异构船舶航行油耗算例进行对比实验,结果表明,相比于其他模型,CatBoost联合个性化联邦学习预测方法的预测精度更高,对降低异构船舶航行油耗具有一定的指导意义。

Feedback and Feedforward Optimal Control for Offshore Jacket Platforms

The optimal control is investigated for linear systems affected by external harmonic disturbance and applied to vibration control systems of offshore steel jacket platforms. The wave-induced force is the dominant load that offshore structures are subjected to, and it can be taken as harmonic excitation for the system. The linearized Morison equation is employed to estimate the wave loading. The main result concerns the existence and design of a realizable optimal regulator, which is proposed to damp the forced oscillation in an optimal fashion. For demonstration of the effectiveness of the control scheme, the platform performance is investigated for different wave states. The simulations are based on the tuned mass damper and the active mass damper control devices. It is demonstrated that the control scheme is useful in reducing the displacement response of jacket-type offshore platforms.

Feedforward and Feedback Optimal Control for Linear Systems with Sinusoidal Disturbances

The linear systems affected by additive external sinusoidal disturbances is studied. The problem is to damp this forced oscillation in an optimal fashion. The main result of this paper is a new design approach is proposed of realizable feedforward and feedback optimal control law for a linear time invariant system with sinusoidal disturbances. The algorithm of solving the optimal control law is given. It is shown that the control law is easily realized and is robust with respect to errors produced by the external sinusoidal disturbances through simulation results.