Research and Analysis of Grammatical Error Correction Technology for Chinese Documents

With the widespread use of Chinese globally, the number of Chinese learners has been increasing, leading to various grammatical errors among beginners. Additionally, as domestic efforts to develop industrial information grow, electronic documents have also proliferated. When dealing with numerous electronic documents and texts written by Chinese beginners, manually written texts often contain hidden grammatical errors, posing a significant challenge to traditional manual proofreading. Correcting these grammatical errors is crucial to ensure fluency and readability. However, certain special types of text grammar or logical errors can have a huge impact, and manually proofreading a large number of texts individually is clearly impractical. Consequently, research on text error correction techniques has garnered significant attention in recent years. The advent and advancement of deep learning have paved the way for sequence-to-sequence learning methods to be extensively applied to the task of text error correction. This paper presents a comprehensive analysis of Chinese text grammar error correction technology, elaborates on its current research status, discusses existing problems, proposes preliminary solutions, and conducts experiments using judicial documents as an example. The aim is to provide a feasible research approach for Chinese text error correction technology.

A New Class of Nonlinear Error Control Codes Based on Neural Networks

By mcans of stable attractors of discret Hopfield neural network (DHNN) , anew class of nonlinear error control codes is sugsested and some relativetheorems are presented. A kind of single error control codes is also given forillustrating this new approach.

Stock Price Prediction Using Predictive Error Compensation Wavelet Neural Networks

:Machine Learning(ML)algorithms have been widely used for financial time series prediction and trading through bots.In this work,we propose a Predictive Error Compensated Wavelet Neural Network(PEC-WNN)ML model that improves the prediction of next day closing prices.In the proposed model we use multiple neural networks where the first one uses the closing stock prices from multiple-scale time-domain inputs.An additional network is used for error estimation to compensate and reduce the prediction error of the main network instead of using recurrence.The performance of the proposed model is evaluated using six different stock data samples in the New York stock exchange.The results have demonstrated significant improvement in forecasting accuracy in all cases when the second network is used in accordance with the first one by adding the outputs.The RMSE error is 33%improved when the proposed PEC-WNN model is used compared to the Long ShortTerm Memory(LSTM)model.Furthermore,through the analysis of training mechanisms,we found that using the updated training the performance of the proposed model is improved.The contribution of this study is the applicability of simultaneously different time frames as inputs.Cascading the predictive error compensation not only reduces the error rate but also helps in avoiding overfitting problems.

Homomorphic Error-Control Codes for Linear Network Coding in Packet Networks

In this work, the homomorphism of the classic linear block code in linear network coding for the case of binary field and its extensions is studied. It is proved that the classic linear error-control block code is homomorphic network error-control code in network coding. That is, if the source packets at the source node for a linear network coding are precoded using a linear block code, then every packet flowing in the network regarding to the source satisfies the same constraints as the source. As a consequence, error detection and correction can be performed at every intermediate nodes of multicast flow, rather than only at the destination node in the conventional way, which can help to identify and correct errors timely at the error-corrupted link and save the cost of forwarding error-corrupted data to the destination node when the intermediate nodes are ignorant of the errors. In addition, three examples are demonstrated which show that homomorphic linear code can be combined with homomorphic signature, McEliece public-key cryptosystem and unequal error protection respectively and thus have a great potential of practical utility.

Diminution in Error Approximation by Identity Authentication with IPAS for FTLSP to Enhance Network Security

In this paper, we have proved the diminution in error approximation when identity authentication is done with Ideal Password Authentication Scheme (IPAS) for Network Security. Effectiveness of identity authentication parameters for various attacks and security requirements is verified in the paper. Result of analysis proves that IPAS would enhance the transport layer security. Proof of efficiency of result is generated with drastic diminution in error approximation. IPAS would have advanced security parameters with implemented RNA-FINNT which would result in fortification of the transport layer security protocol for enhancement of Network Security.

Application of the back-error propagation artificial neural network(BPANN) on genetic variants in the PPAR-γ and RXR-α gene and risk of metabolic syndrome in a Chinese Han population

This study was aimed to explore the associations between the combined effects of several polymorphisms in the PPAR-γ and RXR-α gene and environmental factors with the risk of metabolic syndrome by back-error propaga- tion artificial neural network （BPANN）. We established the model based on data gathered from metabolic syndrome patients （n = 1012） and normal controls （n = 1069） by BPANN. Mean impact value （MIV） for each input variable was calculated and the sequence of factors was sorted according to their absolute MIVs. Generalized multifactor dimensionality reduction （GMDR） confirmed a joint effect of PPAR-9＂ and RXR-a based on the results from BPANN. By BPANN analysis, the sequences according to the importance of metabolic syndrome risk fac- tors were in the order of body mass index （BMI）, serum adiponectin, rs4240711, gender, rs4842194, family history of type 2 diabetes, rs2920502, physical activity, alcohol drinking, rs3856806, family history of hypertension, rs1045570, rs6537944, age, rs17817276, family history of hyperlipidemia, smoking, rs1801282 and rs3132291. However, no polymorphism was statistically significant in multiple logistic regression analysis. After controlling for environmental factors, A1, A2, B1 and B2 （rs4240711, rs4842194, rs2920502 and rs3856806） models were the best models （cross-validation consistency 10/10, P = 0.0107） with the GMDR method. In conclusion, the interaction of the PPAR-γ and RXR-α gene could play a role in susceptibility to metabolic syndrome. A more realistic model is obtained by using BPANN to screen out determinants of diseases of multiple etiologies like metabolic syndrome.

改进贝叶斯网络模型在起重作业人机交互差错风险分析中的应用

为量化分析起重作业人机交互差错风险,根据安全工效学原理及安全技术规范将起重作业人、机、环相关影响因素作为根节点,按照事故致因层次关联关系确定子节点,构建起重作业人机交互差错的3层级贝叶斯网络模型(Bayesian Network, BN);基于模糊集理论,采用认知可靠性与失误分析方法(Cognitive Reliability and Error Analysis Method, CREAM),厘定贝叶斯网络父节点失效概率以及中间节点条件概率;利用逆向推理仿真技术分析起重作业人机交互差错发生的因果链,探究起重伤害事故发生的人机交互差错风险。结果表明:起重作业人机交互差错最可能致因链为起重设备安全检查不到位→管理人员失误→人员操作失误→起重伤害事故发生;单因素失效条件下,起重作业人机交互差错风险概率呈线性增长趋势;在多因素失效条件下,一级节点因素失效概率愈大则人机交互差错效应愈显著,且呈现非线性增长态势。

基于改进松鼠搜索算法优化神经网络的数控机床进给系统热误差预测

为探究数控机床进给系统中各因素对热误差的影响规律,建立精准的热误差预测模型。在进给速度为10 m/min、环境温度20℃的条件下进行进给系统热误差测量实验,获得进给系统关键点的温升及热误差。为提高预测精度,采用Tent混沌改进松鼠搜索算法,并利用改进的算法对神经网络进行优化,建立热误差预测模型。利用热误差测量实验获得的数据进行验证,结果表明改进前的神经网络预测误差为12.23%,改进后的模型预测误差为8.92%,精度有较大提升。利用预测模型针对不同进给速度下相同位置处热误差进行分析,结果表明,进给系统中关键测温点的温度和丝杠各点的热误差随着进给速度的增加而增加。因此提出的预测模型可实现进给系统热误差的准确预测,为误差补偿提供理论依据。

Track-to-Track Association Technique in Radar Network in the Presence of Systematic Errors

The presence of systematic measuring errors complicates track-to-track association, spatially separates the tracks that correspond to the same true target, and seriously decline the performances of traditional track-to-track association algorithms. Consequently, the influence of radar systematic errors on tracks from different radars, which is described as some rotation and translation, has been analyzed theoretically in this paper. In addition, a novel approach named alignment-correlation method is developed to estimate and reduce this effect, align and correlate tracks accurately without prior registration using phase correlation technique and statistic binary track correlation algorithm. Monte-Carlo simulation results illustrate that the proposed algorithm has good performance in solving the track-to-track association problem with systematic errors in radar network and could provide effective and reliable associated tracks for the next step of registration.

基于神经网络的双轴音圈电机驱动系统智能控制

在双轴或多轴驱动伺服系统中,各轴之间的动态特性互相耦合,严重影响了平台的定位精度和轮廓精度,而传统控制方法运算量大,寻找最优值困难。该文在基于设定值前向补偿的交叉耦合控制方法的基础上,提出了一种基于神经网络的智能控制策略。利用S函数实现了神经网络的在线调整、参数自动调整寻优。改变了系统学习速度,增强了其自适应能力,简化了操作复杂度,实现了系统的智能跟踪控制。用Matlab对文中方法进行仿真,并且与其它方法进行比较,结果表明,改进后的方法在减小轮廓误差和提高定位精度等方面有很好的效果,且在实验平台上进一步验证了该控制策略的可行性和有效性。

近红外光谱传感器的测点热误差补偿方法设计

红外光谱传感器测量受测点不合理产生的热误差影响存在测量偏差较大的问题。提出一种近红外光谱传感器测点热误差补偿方法。根据近红外光谱传感器热变形和测点温度之间的关联性,确定近红外光谱传感器最佳测温点。根据偏F统计量的检验在回归模型中加入拟合处理后的温度变量,删除前期无利用价值的温度变量,以此为依据建立测点热误差模型。根据测点热误差模型中的约束条件,对各个测点进行标定处理,同时引入人工神经网络对近红外光谱传感器各个测点进行温度拟合补偿。实验结果表明,所提方法得到的测点热误差补偿量最高值为45μm、对丝杠XYZ三个轴的补偿量与实际值一致、补偿后的误差控制在4.5μm以内。证明采用所提方法可以获取满意的近红外光谱传感器温度补偿结果。

六自由度机器人重力误差识别与补偿方法

为了提高六自由度关节机器人末端执行器的位置精度,文章基于自主设计研发的六自由度关节机器人进行重力误差识别和补偿方法研究。首先,基于D-H法建立机器人各关节转角与末端位置坐标的关系模型,提出采用Levenberg-Marquardt进行主要关节实际扭转刚度的计算方法;其次,基于有限元分析软件,进行不同位姿下机器人在重力场作用下的静力学分析,建立机器人位姿参数为输入、末端变形为输出的BP神经元网络模型,以实现机器人不同位姿下连杆弯曲变形导致的末端位置误差的在线预测;最后,基于自主设计的专用测头进行机器人末端位置误差补偿实验。实验数据表明,补偿后的定位精度比补偿前在X和Z方向分别提高了93.6%和92.2%。

基于风况预测误差自适应的海上风电场尾流偏转控制方法

风电场尾流偏转控制是降低尾流效应、提升整场发电量的重要手段。风况预测值是风电场尾流偏转控制的重要输入,其误差给实际控制效果带来巨大影响,甚至导致全场发电量“不增反降”,极大限制了风电场尾流偏转控制技术的工程应用。以某海上风电场实际运行数据为例,探索了分钟级风速和风向预测误差对风电场尾流偏转控制效果的影响,提出了风况预测误差自适应的海上风电场尾流偏转控制方法及基于深度神经网络的控制模型。研究结果表明:与不考虑风况预测误差自适应的传统风电场尾流偏转控制方法相比,所提方法的全场发电量提高了1.77%。

高速铁路三维控制网粗差实时判别方法研究

高速铁路三维控制网外业观测易受不利环境因素影响,难免存在测量粗差,为了尽早识别测量粗差,提高外业数据质量及后续平差结果的精度,需进行三维控制网粗差实时判别方法的研究。根据三维控制网网形及其特点,详细分析三维控制网闭合环的类型,采用三维控制网(Ⅰ类、Ⅱ类、Ⅲ类)22种闭合环进行闭合差计算;同时,基于协方差传播定律构建闭合环校核边中误差的数学模型,以2倍闭合环相对中误差为限差,判别三维控制网外业数据质量。研究表明,三维控制网网形具有较强的规律性和对称性,在完成归整后,可使用1/7000,1/13000和1/23000为对应闭合环的极限误差进行粗差判别。研究结果表明,在三维控制网外业数据观测过程中,采用该方法能够实时判别并发现测量粗差,有利于以最小代价进行现场补测,从而提高三维控制网数据可靠性和数据质量。

基于漏斗函数双电机伺服系统跟踪与同步控制

针对双电机驱动伺服系统具有未知非线性的问题,提出一种基于漏斗函数的跟踪与同步控制方案。首先,利用神经网络逼近和补偿复杂的非线性,在此基础上,引入滤波技术解决传统反步控制的“计算爆炸”问题,同时引入非光滑漏斗误差面确保系统的状态量被约束在预定义的漏斗边界内,结合改进的漏斗函数和反步设计技术设计了一种自适应量化漏斗跟踪控制方案。为了同时保证双电机的同步运行,同步控制器采用了平均偏差耦合策略,实现了双电机伺服系统的跟踪与同步控制。仿真结果表明,该方法可以实现对负载的跟踪以及双电机的同步。

数控机床旋转轴多自由度静/热误差同步测量与建模

针对现有的数控机床旋转轴误差测量与建模方法仅考虑多自由度静态几何误差或单自由度热误差单独作用的影响,未考虑几何误差和热误差耦合影响的问题,提出了一种基于球杆仪的数控机床旋转轴多自由度静/热误差同步测量与建模方法。首先基于齐次坐标变换建立球杆仪杆长变化模型,再基于该模型使用非齐次线性方程组建立静/热误差辨识模型;其次设计了适应多自由度静/热误差同步测量的球杆仪安装模式以缩短测量时间,减少热逸散对测量结果的影响;再次基于卷积长短期记忆神经网络(CNN-LSTM)建立旋转轴多自由度静/热误差预测模型;最后在数控蜗杆砂轮磨齿机的C轴上进行误差测量实验,对多种转速下的旋转轴多自由度误差进行快速辨识,并通过CNN-LSTM静/热误差预测模型对多自由度误差和球杆仪杆长变化进行预测,以验证所建模型的准确性。

全状态约束下长行程混联机器人投影迭代鲁棒控制算法

针对全状态约束下的长行程混联机器人系统鲁棒性较差提出了基于自适应学习神经网络和等效误差函数的投影迭代鲁棒控制算法。基于自适应学习神经网络逼近未知的非线性项,提出投影迭代鲁棒控制算法,更新网络权值并估计逼近误差和随机外部扰动的未知上界;构造用于抵消初始时刻随机变化扩展误差的时变边界层,设计基于时变边界层和扩展误差的等效误差函数作为迭代控制器的主要控制变量以克服随机初始误差满足相同初始条件;在控制器设计中引入正切型障碍Lyapunov函数,确保系统状态在预定范围内。仿真实验结果证明了该方法的有效性,可在全状态约束下实现高精度强鲁棒性的轨迹跟踪。

机器学习的高精度毫米波雷达测距信号误差补偿方法

毫米波雷达是一种常用的非接触式测距技术,受环境因素以及测量过程中存在的各种误差影响,测距结果可能存在一定的误差。研究误差补偿方法可以有效提高毫米波雷达的测距精度,从而更准确地获取目标物体的距离信息。为此,提出了机器学习的高精度毫米波雷达测距信号误差补偿方法。通过高斯滤波器去除雷达测距信号中的噪声,完成信号的去噪处理。利用模拟插入脉冲计数法和四象限光斑定位法,测量目标物体的距离和角度信息,通过自适应惯性权重与收敛因子优化粒子群算法,并利用优化后的粒子群算法改进BP神经网络,将测量的距离和角度信息输入到改进的BP神经网络中展开训练,即可得到补偿后的雷达测距信号。实验结果表明,该方法的信号处理效果好,补偿后的毫米波雷达测距信号方位角和俯仰角误差接近于0,且信号平滑度较高。

铁路时间同步网节点天地时间源互备失败案例分析

通过二级时间节点的天地时间源互备失败现象,分析天地时间源互备失败原因。主要原因为天地时间源间时间偏差过大,从理论上计算长距离地面时间传送可能引入的时间误差,并分析造成时间误差过大的几种可能原因。由此得出,现有铁路时间同步网技术架构难以满足将来5G-R系统对高精度时间同步性能和安全可靠运维要求,有必要研究完善铁路时间同步网技术架构、组网方案,并进一步健全运维方法。