Motor Imagery and Error Related Potential Induced Position Control of a Robotic Arm

The paper introduces an electroencephalography(EEG) driven online position control scheme for a robot arm by utilizing motor imagery to activate and error related potential(ErrP) to stop the movement of the individual links, following a fixed(pre-defined) order of link selection. The right(left)hand motor imagery is used to turn a link clockwise(counterclockwise) and foot imagery is used to move a link forward. The occurrence of ErrP here indicates that the link under motion crosses the visually fixed target position, which usually is a plane/line/point depending on the desired transition of the link across 3D planes/around 2D lines/along 2D lines respectively. The imagined task about individual link's movement is decoded by a classifier into three possible class labels: clockwise, counterclockwise and no movement in case of rotational movements and forward, backward and no movement in case of translational movements. One additional classifier is required to detect the occurrence of the ErrP signal, elicited due to visually inspired positional link error with reference to a geometrically selected target position. Wavelet coefficients and adaptive autoregressive parameters are extracted as features for motor imagery and ErrP signals respectively. Support vector machine classifiers are used to decode motor imagination and ErrP with high classification accuracy above 80%. The average time taken by the proposed scheme to decode and execute control intentions for the complete movement of three links of a robot is approximately33 seconds. The steady-state error and peak overshoot of the proposed controller are experimentally obtained as 1.1% and4.6% respectively.

A new calculation approach of wellbore separation factor based on the relative position of adjacent wells

As the current calculation methods for wellbore separation factor have some deficiencies, we propose and analyze a new calculation approach for wellbore separation factor based on the relative position of adjacent wellbores, named as relative position method for short. Based on the trajectory error ellipsoid model of single wellbore, the error ellipsoids model of adjacent wellbore was derived considering the correlation of trajectory errors between adjacent wells. Furthermore, the calculation formula of the separation factor based on relative position of adjacent wellbore was derived and solved with the conjugate gradient algorithm. Case study shows that the new approach is more precise and higher in applicability than the ellipsoid scaling method and the minimum distance method, it can evaluate the state of well collision more reasonably. By doing batch calculation with the new method and following the criterion of well collision avoidance, the permissible ranges of key parameters in the well design can be worked out quickly. This method has good application in the design of cluster wells and directional wells.

轻度认知功能障碍患者不同频率重复经颅磁刺激康复治疗效果研究

目的:观察不同频率重复经颅磁刺激(rTMS)对老年轻度认知功能障碍(MCI)患者精神行为症状的影响以及康复治疗效果评价。方法:采用随机数字表法,将90例伴有精神行为症状的MCI患者随机分为3组,分别给予5 Hz、10 Hz和15 Hz的rTMS治疗6周(30次),分别于治疗前后测定并比较患者简易精神状态检查(MMSE)分值、P300、错误相关负电位(ERN)的潜伏期和波幅。结果:与治疗前相比:(1)10 Hz组患者rTMS治疗后MMSE量表分值明显增高(P<0.05),而5 Hz和15 Hz组MMSE量表分值无明显改善;(2)10 Hz组患者rTMS治疗后P300和ERN波幅提高(P均<0.05);潜伏期两组差异无统计学意义(P>0.05),其余两组治疗前后P300和ERN潜伏期和波幅均未见明显变化。(3)10 Hz患者组MMSE分值的改善状况与患者年龄及病程有关。结论:高频10 Hz的rTMS对MCI患者精神行为症状具有明显缓解作用,同时能改善患者认知功能,该高频疗法值得临床进一步推广。P300和ERN可用于随访评估。

动机在错误加工中的不一致效应:争议与整合

错误加工(error processing)是个体探测错误发生并进行针对性调整的高级认知过程。大量研究表明,动机(如奖励、惩罚等)能够影响错误加工过程,但结果并不一致。本文在梳理和比较以往研究的基础上提出,年龄、性别、实验设计、人格和文化等因素可能影响动机与错误加工的关系。通过进一步整合,提出任务相关性(task relevance)可能是潜在的共同作用机制,即在与个体目标高相关的任务中,动机越强则错误加工程度增强(如更大的ERN、Pe波幅);而在低相关任务中,动机对错误加工的影响较弱。在这一理论框架下,上述因素可能通过影响任务相关性进而调节动机与错误加工的关系,新观点的提出有助于解释动机与错误加工关系的矛盾结果。未来一是需要系统验证上述因素及任务相关性的作用,二是关注不同类型错误和错误本身的动机作用,三是进一步探究动机与认知因素如何共同影响错误加工,这些将有助于完善和推进动机取向的错误加工理论。

基于疾病诊断相关分组付费原理分析乳腺癌手术操作编码的错误案例

目的为分析基于病病诊断相关分组(DRGs)分组付费原理分析乳腺癌手术操作编码的错误案例以减少手术操作编码错误的情况。方法本研究在2019年7月至2023年7月期间于在我院接受乳腺癌手术治疗的患者为研究对象,基于DRGs分组原理收集了乳腺癌手术治疗的患者380例,并对这些患者的病案数据进行核查,收集核查结果包括不同手术方式以及不同分期患者中错误编码的发生率情况、错误编码的类别分布以及错误编码的责任人情况。结果通过对380例患者的病案数据核查,发现其中有错误编码案例44例,不同手术方式以及不同分期患者中错误编码的发生率情况差异无统计学意义(χ^(2)=2.504,P>0.05),编码不完整错误有5例,编码不准确有17例、编码不规范有18例、编码不一致有4例,分别占错误编码的比例11.4%、38.6%、40.9%以及9.1%;临床医师发生错误编码情况有39例占总错误的比例为88.6%、编码员发生错误情况有5例占总错误的比例为11.4%。结论我院编码错误类型主要是编码不准确以及编码不规范的问题,并且临床医师发生错误编码情况较多。

Research of contour error compensation control for X-C non-circular grinding in polar coordinates

In the X-C linkage grinding of non-circular parts,the computation and control method of contour error in polar coordinates platform is different with that in the XY coordinates platform.To solve this problem,the analysis of the definition and computation methods for contour error and track error in polar coordinates platform will be made.Through the relative lead-lag relation of the linkage axes in the grinding process,the range of the estimation contour error is narrowed and a contour error calculation model is constructed.Then the contour compensation controllers along contour error direction and trajectory tracking error direction are designed respectively,and the error compensation decoupling matrix of the X-C linkage axes is given as well.In the end of this paper,we take the machining of the cylinder contour in Wankel rotary piston engine as an example.A simulation experiment of contour error compensation control based on relative lead-lag quantity is made.The result shows that the designed contour compensation controller can increase the contour machining accuracy effectively.

Detecting and Repairing Data-Flow Errors in WFD-net Systems

Workflow system has become a standard solution for managing a complex business process.How to guarantee its correctness is a key requirement.Many methods only focus on the control-flow verification,while they neglect the modeling and checking of data-flows.Although some studies are presented to repair the data-flow errors,they do not consider the effect of delete operations or weak circulation relations on the repairing results.What’s more,repairing some data-flow errors may bring in new errors.In order to solve these problems,we use workflow net with data(WFD-net)systems to model and analyze a workflow system.Based on weak behavioral relations and order relations in a WFD-net system,we formalize four kinds of data-flow errors.After then,we reveal the relations between these errors and organize them into a hierarchy.Furthermore,we propose some new methods to repair data-flow errors in a WFD-net system based on system requirements and repair strategies.Finally,a case study of campus-card recharging shows the applicability of our methods,and a group of experiments show their advantages and effectiveness.

The Satellite’s On-Orbit Attitude System Error Compensation Technique Based on Stereo Models

According to the problem that the low measurement accuracy of TH-1 satellite star sensor, the low frequency and “slow drift” error which cannot be ignored in the attitude determination system, resulting in obvious random error in the horizontal position and elevation direction, and the change of the error with time and latitude, cannot be calibrated by the ground field of the real problem. In this paper, a low frequency detection model is established by using the principle of relative orientation, and the low frequency error is obtained by parallax elimination. Finally, the satellite attitude is compensated and the more accurate exterior orientation elements are obtained, thus improving the positioning accuracy and stability. The experimental results show that: the proposed methods are feasible, and by using the model to dynamically calibrate the exterior orientation angle elements on orbit, the plane and elevation errors of the ground points can be basically eliminated. The global uncontrollable positioning accuracy and stability of the photogrammetry satellite are improved.

An Approach to Reasoning Topological Relations Between Areal Objects Under Randomness

This paper describes the geometric and statistical properties of areal object under randomness. In order to describe formally such a uncertain topological relation, a new formal model (i.e. 4ID model) is proposed. On the basis of this, the effects of positional uncertainty on topological relations between areal objects are investigated in detail. Some possibility functions for the determination of relations are constructed based on the assumption that randomness of point location complies with a normal distribution, and the concept of uncertain sets of topological relations under randomness is introduced.

Relation between the Actual and Estimated Maximum Ground Level Concentration of Air Pollutant and Its Downwind Locations

In this work we used the Gaussian plume model to calculate the actual maximum ground level concentration (MGLC) of air pollutant and its downwind location by using different systems of dispersion parameters and for different stack heights. An approximate formula for the prediction of downwind position that produces the MGLC of a pollutant based on the Gaussian formula was derived for different diffusion parameters. The derived formula was used to calculate the approximate MGLC. The actual and estimated values are presented in tables. The comparison between the actual and estimated values was investigated through the calculation of the relative errors. The values of the relative errors between the actual and estimated MGLC lie in the range from: 0 to 70.2 and 0 to 1.6 for Pasquill Gifford system and Klug system respectively. The errors between the actual and estimated location of the MGLC lies in the range from: 0.2 to 227 and 0.7 to 9.4 for Pasquill Gifford system and Klug system respectively.

基于CNN-GRU组合神经网络的数控机床进给系统热误差研究

热变形引起的误差是影响数控机床精度的主要因素之一。为了减小热误差对数控机床精度的影响,提出一种基于CNN-GRU组合神经网络的热误差预测方法。通过热误差实验,采集螺旋曲面专用数控机床直线进给系统的温升数据和热误差数据;利用模糊C均值聚类和灰色关联度分析筛选进给系统温度敏感点;以温度敏感点的温升数据和进给系统热误差为数据样本,建立CNN-GRU热误差预测模型。为验证模型的准确性和实用性,与基于CNN-LSTM和基于LSTM的传统热误差预测模型进行预测对比分析,结果表明CNN-GRU模型预测结果的平均绝对误差、均方根误差和决定系数均优于CNN-LSTM模型和LSTM模型,具有较高的预测精度和鲁棒性。提供的热误差模型可为后续误差补偿奠定基础,为数控机床的热误差预测提供思路。

Clinical decision support for drug related events: Moving towards better prevention

Clinical decision support(CDS) systems with automated alerts integrated into electronic medical records demonstrate efficacy for detecting medication errors(ME) and adverse drug events(ADEs). Critically ill patients are at increased risk for ME, ADEs and serious negative outcomes related to these events. Capitalizing on CDS to detect ME and prevent adverse drug related events has the potential to improve patient outcomes. The key to an effective medication safety surveillance system incorporating CDS is advancing the signals for alerts by using trajectory analyses to predict clinical events, instead of waiting for these events to occur. Additionally, incorporating cutting-edge biomarkers into alert knowledge in an effort to identify the need to adjust medication therapy portending harm will advance the current state of CDS. CDS can be taken a step further to identify drug related physiological events, which are less commonly included in surveillance systems. Predictive models for adverse events that combine patient factors with laboratory values and biomarkers are being established and these models can be the foundation for individualized CDS alerts to prevent impending ADEs.

Dynamic Relation Mechanism between Cotton Future Price and Stock Price of Related Listed Companies

The Dynamic relation mechanism between ZCE cotton futures price and related listed company stock price has been studied based on the metastock historical data in January 1st,2007 to September 1st,2010,Johansen co-integration analysis,Vector error correction model,Granger causality test and variance decomposition method.The results indicated that:long-term equilibrium relationship existed between ZCE cotton futures price and Xinsai share stock price while which changed in the same tendency and speed in the long-term.Cotton futures price is the main reason for the changing of Xinsai share stock price.The lead-lag relationship in changing course had been confirmed that existed between ZCE cotton futures price and the Xinsai share stock price.Meanwhile,the forward pass mechanism of price changing information had been found only from the ZCE cotton futures market to the stock market while showing asymmetry.Conclusions of the study can be used for cotton and related corporate to hedge business risks by the cotton price changes.

基于GRA-ISSA-SVR-EC模型的风电功率组合预测方法

针对风电功率的非线性特征,本文提出一种结合灰色关联度分析(GRA)、改进麻雀算法(ISSA)、支持向量回归(SVR)和误差修正模型(EC)的风电功率组合预测方法。应用GRA筛选出与风电功率关联度较大的影响因素作为模型的输入;引入自适应权重因子和Levy飞行策略提升传统SSA的性能,建立ISSA-SVR模型得到初始预测值;建立误差修正模型进行预测得到误差预测值,最后将初始预测值与误差预测值用加法器合并得到最终结果。仿真结果表明,该模型在对2个风电场的风电功率进行预测时,平均确定系数分别达到0.9996和0.9985,平均绝对误差分别为0.2266 kW和0.0146 MW,均方根误差分别为0.2777 kW和0.0213 MW,相比于其他传统模型预测精度更高。

基于P300与ErrP决策融合的脑-机接口目标检测方法

针对脑-机接口(BCI)技术在目标检测中的应用仍然存在检测准确率受限的问题,提出基于事件相关电位(ERP)中的P300与错误相关电位(ErrP)决策融合的新型编解码方法。BCI系统编码方面通过目标图像和视觉反馈分别诱发P300与ErrP特征,解码方面采用单独P300特征、单独ErrP特征、P300与ErrP特征层融合、P300与ErrP决策层融合这4种方案进行目标检测。10名健康受试者4种方案进行目标检测的平均结果显示,使用P300与ErrP决策层融合的平衡正确率最高,达到80.03%±5.20%,相比单独使用P300特征的方法提升了4.38%,相比单独使用ErrP特征的方法提升了11.29%,验证了混合BCI技术在目标检测任务中的可行性。

规范化管理住院病案首页信息在DRGs准确分组中的应用价值

目的探讨规范化管理住院病案首页信息在疾病诊断相关分组(DRGs)准确分组中的应用价值。方法回顾性分析2022年1月至12月无锡市第二人民医院内科接受管理的132例病案,将采取传统常规DRGs管理的66例病案划分为对照组,其中男34例,女32例,年龄22~49(38.65±6.36)岁;采取规范化管理住院病案首页信息的66例病案划分为试验组,其中男35例,女31例,年龄21~48(38.23±6.25)岁。对比两组DRGs准确分组情况、医护人员满意度和DRGs分组差错率。统计学方法采用χ^(2)检验、t检验。结果试验组DRGs分组规范率高于对照组,病案返修率低于对照组[81.82%(54/66)比60.61%(40/66)、6.06%(4/66)比19.70%(13/66)],差异均有统计学意义(χ^(2)=7.246、5.469,均P<0.05)。试验组医护人员满意度评分高于对照组[(25.36±1.38)分比(20.21±1.14)分],差异有统计学意义(t=5.854,P<0.05)。试验组差错的总发生率低于对照组[4.55%(3/66)比18.18%(12/66)],差异有统计学意义(χ^(2)=6.092,P<0.05)。结论规范化管理住院病案首页信息应用于DRGs准确分组中,可提高病案质量与医护人员满意度,减少差错的总发生率,值得医疗机构推广应用。

Output Linearization of Single-Input Single-Output Fuzzy System to Improve Accuracy and Performance

For fuzzy systems to be implemented effectively,the fuzzy membership function(MF)is essential.A fuzzy system(FS)that implements precise input and output MFs is presented to enhance the performance and accuracy of single-input single-output(SISO)FSs and introduce the most applicable input and output MFs protocol to linearize the fuzzy system’s output.Utilizing a variety of non-linear techniques,a SISO FS is simulated.The results of FS experiments conducted in comparable conditions are then compared.The simulated results and the results of the experimental setup agree fairly well.The findings of the suggested model demonstrate that the relative error is abated to a sufficient range(≤±10%)and that the mean absolute percentage error(MPAE)is reduced by around 66.2%.The proposed strategy to reduceMAPE using an FS improves the system’s performance and control accuracy.By using the best input and output MFs protocol,the energy and financial efficiency of every SISO FS can be improved with very little tuning of MFs.The proposed fuzzy system performed far better than other modern days approaches available in the literature.

Energy Efficient and Intelligent Mosquito Repellent Fuzzy Control System

Mosquitoes are of great concern for occasionally carrying noxious diseases(dengue,malaria,zika,and yellow fever).To control mosquitoes,it is very crucial to effectively monitor their behavioral trends and presence.Traditional mosquito repellent works by heating small pads soaked in repellant,which then diffuses a protected area around you,a great alternative to spraying yourself with insecticide.But they have limitations,including the range,turning them on manually,and then waiting for the protection to kick in when the mosquitoes may find you.This research aims to design a fuzzy-based controller to solve the above issues by automatically determining a mosquito repellent’s speed and active time.The speed and active time depend on the repellent cartridge and the number of mosquitoes.The Mamdani model is used in the proposed fuzzy system(FS).The FS consists of identifying unambiguous inputs,a fuzzification process,rule evaluation,and a defuzzification process to produce unambiguous outputs.The input variables used are the repellent cartridge and the number of mosquitoes,and the speed of mosquito repellent is used as the output variable.The whole FS is designed and simulated using MATLAB Simulink R2016b.The proposed FS is executed and verified utilizing a microcontroller using its pulse width modulation capability.Different simulations of the proposed model are performed in many nonlinear processes.Then,a comparative analysis of the outcomes under similar conditions confirms the higher accuracy of the FS,yielding a maximum relative error of 10%.The experimental outcomes show that the root mean square error is reduced by 67.68%,and the mean absolute percentage error is reduced by 52.46%.Using a fuzzy-based mosquito repellent can help maintain the speed of mosquito repellent and control the energy used by the mosquito repellent.

Local Least Product Relative Error Estimation for Varying Coefficient Multiplicative Regression Model

In this article, we consider the varying coefficient multiplicative regression model, which is very useful to model the positive response. The criterion of least product relative error(LPRE) is extended to the varying coefficient multiplicative regression model by kernel smoothing techniques. Consistency and asymptotic normality of the proposed estimator are established. Some numerical simulations are carried out to assess the performance of the proposed estimator. As an illustration, the ethanol data is analyzed.

新能源并网对电网电能质量的影响分析方法

针对新能源并网对电网电能质量的影响问题,通过设置不同的函数,实现了新能源并网影响电能质量的特性量化分析。并将新能源并网对电网电能质量的影响转换为数学形式的思维,以量化的方式解决实际问题,提高了实际问题分析能力。文章还构建了灰色关联模型,以便用户通过一种数据影响量分析其他数据影响量。通过试验,提出的方法评估误差低。