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.

Online Capacitor Voltage Transformer Measurement Error State Evaluation Method Based on In-Phase Relationship and Abnormal Point Detection

The assessment of the measurement error status of online Capacitor Voltage Transformers (CVT) within the power grid is of profound significance to the equitable trade of electric energy and the secure operation of the power grid. This paper advances an online CVT error state evaluation method, anchored in the in-phase relationship and outlier detection. Initially, this method leverages the in-phase relationship to obviate the influence of primary side fluctuations in the grid on assessment accuracy. Subsequently, Principal Component Analysis (PCA) is employed to meticulously disentangle the error change information inherent in the CVT from the measured values and to compute statistics that delineate the error state. Finally, the Local Outlier Factor (LOF) is deployed to discern outliers in the statistics, with thresholds serving to appraise the CVT error state. Experimental results incontrovertibly demonstrate the efficacy of this method, showcasing its prowess in effecting online tracking of CVT error changes and conducting error state assessments. The discernible enhancements in reliability, accuracy, and sensitivity are manifest, with the assessment accuracy reaching an exemplary 0.01%.

Near field 3-D imaging approach for joint high-resolution imaging and phase error correction

This paper combines compressed sensing (CS) imaging theory and range migration algorithm (RMA), and then proposes a near-field three-dimensional (3-D) imaging approach for joint high-resolution imaging and phase error correction. Firstly, a sparse measurement matrix construction method based on a logistic sequence is proposed, which conducts nonlinear transformation for the determined logistic sequence, making it obey uniform distribution, then conducts sign function mapping, and generates the pseudorandom sequence with Bernoulli distribution, thus leading to good signal recovery under down-sampling and easy availability for engineering realization. Secondly, in combination with the RMA imaging approach, the dictionary with all scene information and phase error correction is constructed for CS signal recovery and error correction. Finally, the non-quadratic solution model jointing imaging and phase error correction based on regularization is built, and it is solved by two steps - the separable surrogate functionals (SSF) iterative shrinkage algorithm is adopted to realize target scattering estimate; the iteration mode is adopted for the correction of the dictionary model, so as to achieve the goal of error correction and highly-focused imaging. The proposed approach proves to be effective through numerical simulation and real measurement in anechoic chamber. The results show that, the proposed approach can realize high-resolution imaging in the case of less data; the designed measurement matrix has better non-coherence and easy availability for engineering realization. The proposed approach can effectively correct the phase error, and achieve highly-focused target image. © 2017 Beijing Institute of Aerospace Information.

A Meaningful Image Encryption Algorithm Based on Prediction Error and Wavelet Transform

Image encryption(IE)is a very useful and popular technology to protect the privacy of users.Most algorithms usually encrypt the original image into an image similar to texture or noise,but texture and noise are an obvious visual indication that the image has been encrypted,which is more likely to cause the attacks of enemy.To overcome this shortcoming,many image encryption systems,which convert the original image into a carrier image with visual significance have been proposed.However,the generated cryptographic image still has texture features.In line with the idea of improving the visual quality of the final password images,we proposed a meaningful image hiding algorithm based on prediction error and discrete wavelet transform.Lots of experimental results and safety analysis show that the proposed algorithm can achieve high visual quality and ensure the security at the same time.

Error modeling of 3-RSR parallel robot based on D-H transformation matrix

By selecting any one limb of 3-RSR parallel robot as a research object, the paper establishes a position and orienta- tion relationship matrix between the moving platform and the base by means of Denavit-Hartenberg （D-H） transformation matrix. The error mapping model is derived from original error to the error of the platform by using matrix differential method. This model contains all geometric original errors of the robot. The nonlinear implicit function relation between po- sition and orientation error of the platform and the original geometric errors is simplified as a linear explicit function rela- tion. The results provide a basis for further studying error analysis and error compensation.

STUDY ON THE COMPENSATION TECHNIQUE OF POSITIONING ERRORS FOR NC MACHINE TOOLS

Through analysis of the basic transformation of a typical body,the error transformations of the position vector and the displacement vector are employed,a general model for positioning errors of NC machine tools by using kinematics of the multi body system is discussed.By means of 8031 single chip system,intelligent error compensation controller has been developed.The results of experiments on XH714 machining center show that the positioning accuracy is enhanced effectively by more than 50%.

基于Android平台的实时图像视频水印处理方法

针对现有研究的算法很难用在智能手机等低计算性能设备上的问题,提出基于Android平台的实时图像视频水印处理方法.该方法基于离散余弦变换(DCT),采用预定义的水印对智能手机相机拍摄的图像及视频进行实时水印添加,将水印嵌入实时捕获的宿主图像中.基于Android平台开发APP应用程序,使用默认的智能手机摄像头捕捉图像,通过OpenCV实现实时视频处理和数字水印嵌入处理,并将图像保存到外部存储中.实验结果表明,所提方法基于不同的质量因子均能保持较稳定的峰值信噪比(PSNR)和平均平方误差(MSE),能够较好地适用于智能手机等低计算性能设备.

Denoising of an Image Using Discrete Stationary Wavelet Transform and Various Thresholding Techniques

Image denoising has remained a fundamental problem in the field of image processing. With Wavelet transforms, various algorithms for denoising in wavelet domain were introduced. Wavelets gave a superior performance in image denoising due to its properties such as multi-resolution. The problem of estimating an image that is corrupted by Additive White Gaussian Noise has been of interest for practical and theoretical reasons. Non-linear methods especially those based on wavelets have become popular due to its advantages over linear methods. Here I applied non-linear thresholding techniques in wavelet domain such as hard and soft thresholding, wavelet shrinkages such as Visu-shrink (non-adaptive) and SURE, Bayes and Normal Shrink (adaptive), using Discrete Stationary Wavelet Transform (DSWT) for different wavelets, at different levels, to denoise an image and determine the best one out of them. Performance of denoising algorithm is measured using quantitative performance measures such as Signal-to-Noise Ratio (SNR) and Mean Square Error (MSE) for various thresholding techniques.

Effect of Modulation Error on All Optical Fiber Current Transformers

For actively modulated In-line Sagnac interferential all optic fiber current transformers (AOFCTs), the accuracies are directly affected by the amplitude of the modulation signal. In order to deeply undertand the function of the modulator, a theoretical model of modulation effect to AOFCTs is built up in this paper. The effect of the amplitude of the modulation signal to the output intensity of AOFCTs is theoretically formulated and numerical calculated. The results show that the modulation voltage variation could affect the output accuracies significantly. This might be some references on the investigation for practical applications of AOFCTs.

Performance Comparison of DCT and VQ Based Techniques for Iris Recognition

Iris recognition enjoys universality, high degree of uniqueness and moderate user co-operation. This makes iris recognition systems unavoidable in emerging security ＆ authentication mechanisms. An iris recognition system based on vector quantization （VQ） techniques is proposed and its performance is compared with the discrete cosine transform （DCT）. The proposed system does not need any pre-processing and segmentation of the iris. We have tested Linde-Buzo- Gray （LBG）, Kekre＇s proportionate error （KPE） algorithm and Kekre＇s fast codebook generation （KFCG） algorithm for the clustering purpose. Proposed vector quantization based method using KFCG requires 99.99% less computations as that of full 2-dimensional DCT. Further, the KFCG method gives better performance with the accuracy of 89.10% outperforming DCT that gives accuracy around 66.10%.

A Class of Estimators for Population Ratio in Simple Random Sampling Using Variable Transformation

This paper is an extension and generalization of the study carried out by [1] on the estimation of the population ratio (R) of the population means of two variables (y and x) under Simple Random Sampling (SRS) scheme, using a variable transformation of the auxiliary variable, x. All the six estimators proposed by [1] are easily identified as special cases of the proposed class of estimators. Asymptotic properties of the proposed class of estimators are derived theoretically and subsequently verified using empirical illustrations. Some of the proposed estimators are found to have relatively large gains in efficiency over the customary ratio estimator, ?for the given data set.

ENTIRE FUNCTIONS REPRESENTED BY LAPLACE-STIELTJES TRANSFORMS CONCERNING THE APPROXIMATION AND GENERALIZED ORDER

The first aim of this paper is to investigate the growth of the entire function defined by the Laplace-Stieltjes transform converges on the whole complex plane.By introducing the concept of generalized order,we obtain two equivalence theorems of Laplace-Stiettjes transforms related to the generalized order,A_(n)^(*)andλ_(n).The second purpose of this paper is to study the problem on the approximation of this Laplace-Stieltjes transform.We also obtain some theorems about the generalized order,the error,and the coefficients of Laplace-Stieltjes transforms,which are generalization and improvement of the previous results.

Background Error Covariance Statistics of Hydrometeor Control Variables Based on Gaussian Transform

Use of data assimilation to initialize hydrometeors plays a vital role in numerical weather prediction(NWP).To directly analyze hydrometeors in data assimilation systems from cloud-sensitive observations,hydrometeor control variables are necessary.Common data assimilation systems theoretically require that the probability density functions(PDFs)of analysis,background,and observation errors should satisfy the Gaussian unbiased assumptions.In this study,a Gaussian transform method is proposed to transform hydrometeors to more Gaussian variables,which is modified from the Softmax function and renamed as Quasi-Softmax transform.The Quasi-Softmax transform method then is compared to the original hydrometeor mixing ratios and their logarithmic transform and Softmax transform.The spatial distribution,the non-Gaussian nature of the background errors,and the characteristics of the background errors of hydrometeors in each method are studied.Compared to the logarithmic and Softmax transform,the Quasi-Softmax method keeps the vertical distribution of the original hydrometeor mixing ratios to the greatest extent.The results of the D′Agostino test show that the hydrometeors transformed by the Quasi-Softmax method are more Gaussian when compared to the other methods.The Gaussian transform has been added to the control variable transform to estimate the background error covariances.Results show that the characteristics of the hydrometeor background errors are reasonable for the Quasi-Softmax method.The transformed hydrometeors using the Quasi-Softmax transform meet the Gaussian unbiased assumptions of the data assimilation system,and are promising control variables for data assimilation systems.

Voltage and Current Mode Vector Analyses of Correction Procedure Application to Clarke’s Matrix—Symmetrical Three-Phase Cases

Clarke’s matrix has been applied as a phase-mode transformation matrix to three-phase transmission lines substituting the eigenvector matrices. Considering symmetrical untransposed three-phase lines, an actual symmetrical three-phase line on untransposed conditions is associated with Clarke’s matrix for error and frequency scan analyses in this paper. Error analyses are calculated for the eigenvalue diagonal elements obtained from Clarke’s matrix. The eigenvalue off-diagonal elements from the Clarke’s matrix application are compared to the correspondent exact eigenvalues. Based on the characteristic impedance and propagation function values, the frequency scan analyses show that there are great differences between the Clarke’s matrix results and the exact ones, considering frequency values from 10 kHz to 1 MHz. A correction procedure is applied obtaining two new transformation matrices. These matrices lead to good approximated results when compared to the exact ones. With the correction procedure applied to Clarke’s matrix, the relative values of the eigenvalue matrix off-diagonal element obtained from Clarke’s matrix are decreased while the frequency scan results are improved. The steps of correction procedure application are detailed, investigating the influence of each step on the obtained two new phase-mode transformation matrices.

A Study on Measurement Error during Alternating Current Induced Voltage Tests on Large Transformers

The large transformer is pivotal equipment in an electric power supply system; Its partial discharge test and the induced voltage withstand test on large transformers are carried out at a frequency about twice the working frequency. If the magnetizing inductance cannot compensate for the stray capacitance, the test sample turns into a capacitive load and a capacitive rise exhibits in the testing circuit. For self-restoring insulation, a method has been recommended in IEC60-1 that an unapproved measuring system be calibrated by an approved system at a voltage not less than 50% of the rated testing voltage, and the result then be extrapolated linearly. It has been found that this method leads to great error due to the capacitive rise if it is not correctly used during a withstand voltage test under certain testing conditions, especiaUy for a test on high voltage transformers with large capacity. Since the withstand voltage test is the most important means to examine the operation reliability of a transformer, and it can be destructive to the insulation, a precise measurement must be guaranteed. In this paper a factor, named as the capacitive rise factor, is introduced to assess the rise. The voltage measurement error during the calibration is determined by the parameters of the test sample and the testing facilities, as well as the measuring point. Based on theoretical analysis in this paper, a novel method is suggested and demonstrated to estimate the error by using the capacitive rise factor and other known parame- ters of the testing circuit.

IDENTIFICATION OF PHASE ERROR BY MULTI-SAMPLING DIGITAL TANLOCKED LOOP(MS-DTL)

A multisampling technique was introduced to the phase error detector of Conventional Digital Tanlocked Loop(C DTL). In this new technique a number of samples ( M ) were taken at nonuniform intervals within one period of the input signal. The novel system is called Multisampling Digital Tanlocked loop(MS DTL). The simulated model on the computer shows that for M =4, the system has a faster locking speed and wider locking in range compared with C DTL.

A METHOD FOR THE MEASUREMENT OF DYNAMIC TRANSMISSION ERROR OF PRECISION HOBBING MACHINES

In the paper,a new method for the measurement of the dynamic transmission crror of preci-sion hobbing machines using only one sensor is proposed.The dynamic transmission error i ob-tained by demodulating the phase-modulated signal according to the Hilbert transform priple.The results of dynamic testing show that the new method is effective.

Adaptive de-noising method based on wavelet and adaptive learning algorithm in on-line PD monitoring

It is an important step in the online monitoring of partial discharge (PD) to extract PD pulses from various background noises. An adaptive de-noising method is introduced for adaptive noise reduction during detection of PD pulses. This method is based on Wavelet Transform (WT), and in the wavelet domain the noises decomposed at the levels are reduced by independent thresholds. Instead of the standard hard thresholding function, a new type of hard thresholding function with continuous derivative is employed by this method. For the selection of thresholds, an unsupervised learning algorithm based on gradient in a mean square error (MSE) is present to search for the optimal threshold for noise reduction, and the optimal threshold is selected when the minimum MSE is obtained. With the simulating signals and on-site experimental data processed by this method, it is shown that the background noises such as narrowband noises can be reduced efficiently. Furthermore, it is proved that in comparison with the conventional wavelet de-noising method the adaptive de-noising method has a better performance in keeping the pulses and is more adaptive when suppressing the background noises of PD signals.