Error Analysis of English Pronunciation Acquisition by Chinese Non-English Majors

Chinese non-English majors are a large group of English learners.In the process of English pronunciation acquisition,issues such as incomplete phonological knowledge,transfer of mother tongue,and overgeneralization,lead to confusion of phonemes and stress,misunderstanding of syllable structure,and errors of assimilation,drop,and epenthesis.The accuracy of English pronunciation can only be improved by knowing both English and Chinese phonological systems,strengthening the teaching of English phonological knowledge,and adopting various phonological training activities.

Uncertainties of landslide susceptibility prediction: Influences of random errors in landslide conditioning factors and errors reduction by low pass filter method

In the existing landslide susceptibility prediction(LSP)models,the influences of random errors in landslide conditioning factors on LSP are not considered,instead the original conditioning factors are directly taken as the model inputs,which brings uncertainties to LSP results.This study aims to reveal the influence rules of the different proportional random errors in conditioning factors on the LSP un-certainties,and further explore a method which can effectively reduce the random errors in conditioning factors.The original conditioning factors are firstly used to construct original factors-based LSP models,and then different random errors of 5%,10%,15% and 20%are added to these original factors for con-structing relevant errors-based LSP models.Secondly,low-pass filter-based LSP models are constructed by eliminating the random errors using low-pass filter method.Thirdly,the Ruijin County of China with 370 landslides and 16 conditioning factors are used as study case.Three typical machine learning models,i.e.multilayer perceptron(MLP),support vector machine(SVM)and random forest(RF),are selected as LSP models.Finally,the LSP uncertainties are discussed and results show that:(1)The low-pass filter can effectively reduce the random errors in conditioning factors to decrease the LSP uncertainties.(2)With the proportions of random errors increasing from 5%to 20%,the LSP uncertainty increases continuously.(3)The original factors-based models are feasible for LSP in the absence of more accurate conditioning factors.(4)The influence degrees of two uncertainty issues,machine learning models and different proportions of random errors,on the LSP modeling are large and basically the same.(5)The Shapley values effectively explain the internal mechanism of machine learning model predicting landslide sus-ceptibility.In conclusion,greater proportion of random errors in conditioning factors results in higher LSP uncertainty,and low-pass filter can effectively reduce these random errors.

Uncertainties in landslide susceptibility prediction:Influence rule of different levels of errors in landslide spatial position

The accuracy of landslide susceptibility prediction(LSP)mainly depends on the precision of the landslide spatial position.However,the spatial position error of landslide survey is inevitable,resulting in considerable uncertainties in LSP modeling.To overcome this drawback,this study explores the influence of positional errors of landslide spatial position on LSP uncertainties,and then innovatively proposes a semi-supervised machine learning model to reduce the landslide spatial position error.This paper collected 16 environmental factors and 337 landslides with accurate spatial positions taking Shangyou County of China as an example.The 30e110 m error-based multilayer perceptron(MLP)and random forest(RF)models for LSP are established by randomly offsetting the original landslide by 30,50,70,90 and 110 m.The LSP uncertainties are analyzed by the LSP accuracy and distribution characteristics.Finally,a semi-supervised model is proposed to relieve the LSP uncertainties.Results show that:(1)The LSP accuracies of error-based RF/MLP models decrease with the increase of landslide position errors,and are lower than those of original data-based models;(2)70 m error-based models can still reflect the overall distribution characteristics of landslide susceptibility indices,thus original landslides with certain position errors are acceptable for LSP;(3)Semi-supervised machine learning model can efficiently reduce the landslide position errors and thus improve the LSP accuracies.

Error Analysis of A New Higher Order Boundary Element Method for A Uniform Flow Passing Cylinders

A higher order boundary element method(HOBEM)is presented for inviscid flow passing cylinders in bounded or unbounded domain.The traditional boundary integral equation is established with respect to the velocity potential and its normal derivative.In present work,a new integral equation is derived for the tangential velocity.The boundary is discretized into higher order elements to ensure the continuity of slope at the element nodes.The velocity potential is also expanded with higher order shape functions,in which the unknown coefficients involve the tangential velocity.The expansion then ensures the continuities of the velocity and the slope of the boundary at element nodes.Through extensive comparison of the results for the analytical solution of cylinders,it is shown that the present HOBEM is much more accurate than the conventional BEM.

A Brief Study of Second Language Learning Strategies From the Perspective of Error Analysis

Language teaching is not a one-way process.It interacts with language learning in an extremely intricate way.To improve language teaching,we need to take the process of language learning into account.This paper tries to explore and understand what strategies the second language learners consciously or subconsciously adopt during their language learning process through the analyses of the linguistic errors they commit,so as to provide some insights into language teaching practice.

Relationships between Terrain Features and Forecasting Errors of Surface Wind Speeds in a Mesoscale Numerical Weather Prediction Model

Numerical weather prediction(NWP)models have always presented large forecasting errors of surface wind speeds over regions with complex terrain.In this study,surface wind forecasts from an operational NWP model,the SMS-WARR(Shanghai Meteorological Service-WRF ADAS Rapid Refresh System),are analyzed to quantitatively reveal the relationships between the forecasted surface wind speed errors and terrain features,with the intent of providing clues to better apply the NWP model to complex terrain regions.The terrain features are described by three parameters:the standard deviation of the model grid-scale orography,terrain height error of the model,and slope angle.The results show that the forecast bias has a unimodal distribution with a change in the standard deviation of orography.The minimum ME(the mean value of bias)is 1.2 m s^(-1) when the standard deviation is between 60 and 70 m.A positive correlation exists between bias and terrain height error,with the ME increasing by 10%−30%for every 200 m increase in terrain height error.The ME decreases by 65.6%when slope angle increases from(0.5°−1.5°)to larger than 3.5°for uphill winds but increases by 35.4%when the absolute value of slope angle increases from(0.5°−1.5°)to(2.5°−3.5°)for downhill winds.Several sensitivity experiments are carried out with a model output statistical(MOS)calibration model for surface wind speeds and ME(RMSE)has been reduced by 90%(30%)by introducing terrain parameters,demonstrating the value of this study.

Convergence analysis for complementary-label learning with kernel ridge regression

Complementary-label learning(CLL)aims at finding a classifier via samples with complementary labels.Such data is considered to contain less information than ordinary-label samples.The transition matrix between the true label and the complementary label,and some loss functions have been developed to handle this problem.In this paper,we show that CLL can be transformed into ordinary classification under some mild conditions,which indicates that the complementary labels can supply enough information in most cases.As an example,an extensive misclassification error analysis was performed for the Kernel Ridge Regression(KRR)method applied to multiple complementary-label learning(MCLL),which demonstrates its superior performance compared to existing approaches.

R-Factor Analysis of Data Based on Population Models Comprising R- and Q-Factors Leads to Biased Loading Estimates

Effects of performing an R-factor analysis of observed variables based on population models comprising R- and Q-factors were investigated. Although R-factor analysis of data based on a population model comprising R- and Q-factors is possible, this may lead to model error. Accordingly, loading estimates resulting from R-factor analysis of sample data drawn from a population based on a combination of R- and Q-factors will be biased. It was shown in a simulation study that a large amount of Q-factor variance induces an increase in the variation of R-factor loading estimates beyond the chance level. Tests of the multivariate kurtosis of observed variables are proposed as an indicator of possible Q-factor variance in observed variables as a prerequisite for R-factor analysis.

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%.

Dynamical Modeling and Dynamic Characteristics Analysisof a Coaxial Dual-Rotor System

The dual-rotor structure serves as the primary source of vibration in aero-engines. Understanding itsdynamical model and analyzing dynamic characteristics, such as critical speed and unbalanced response, arecrucial for rotor system dynamics. Previous work introduced a coaxial dual-rotor-support scheme for aeroengines,and a physical model featuring a high-speed flexible inner rotor with a substantial length-to-diameter ratiowas designed. Then a finite element (FE) dynamic model based on the Timoshenko beam elements and rigid bodykinematics of the dual-rotor system is modeled, with the Newmark method and Newton–Raphson method used forthe numerical calculation to study the dynamic characteristics of the system. Three different simulation models,including beam-based FE (1D) model, solid-based FE (3D) model, and transfer matrix model, were designed tostudy the characteristics of mode and the critical speed characteristic of the dual-rotor system. The unbalancedresponse of the dual-rotor system was analyzed to study the influence of mass unbalance on the rotor system. Theeffect of different disk unbalance phases and different speed ratios on the dynamic characteristics of the dual-rotorsystem was investigated in detail. The experimental result shows that the beam-based FE model is effective andsuitable for studying the dual-rotor system.

DEVELOPMENT AND ERROR ANALYSIS OF AN INSPECTING SYSTEM FOR MEASURING THE ROTATIONAL INERTIA OF BULLETS

介绍了一种用于测量弹丸转动惯量的高精度测量仪器，该仪器利用双悬丝扭摆机构、光电计时系统提取摆动周期信息，由MCS-51系列单片机组成的测量系统进行数据处理，并由LED显示器测量结果。文中阐述了仪器的工作原理， 推导了所需的计算公式，分析了产生测量误差的因素，进行了定量精度分析，给出了实验结果与测量精度。

Error Analysis and Compensation of Strapdown Inertial Navigation System

Based on error analysis, the influence of error sources on strapdown inertial navigation systems is discussed. And the maximum permissible component tolerances are established. In order to achieve the desired accuracy (defined by circular error probability), the types of appropriate sensors are chosen. The inertial measurement unit (IMU) is composed of those sensors. It is necessary to calibrate the sensors to obtain their error model coefficients of IMU. After calibration tests, the accuracy is calculated by uniform design method and it is proved that the accuracy of IMU is satisfied for the desired goal.

Instantaneous rotation speed measurement and error analysis for variable speed hydraulic system

In order to monitor the working state of piston motor and measure its instantaneous rotation speed accurately, the measuring principle and method of instantaneous rotation speed based on industrial personal computer and data acquisition card are introduced, and the major error source, influence mechanism and processing method of data quantization error are dis- cussed. By means of hybrid programming approach of LabVIEW and MATLAB, the instantaneous rotation speed measurement system for the piston motor in variable speed hydraulic system is designed. The simulation and experimental results show that the designed instantaneous speed measurement system is feasible. Furthermore, the sampling frequency has an important influ- ence on the instantaneous rotation speed measurement of piston motor and higher sampling frequency can lower quantization er- ror and improve measurement accuracy.

Error Analysis in Foreign Language Teaching

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Theoretical Approaches to Error Analysis

Contrast Analysis (CA), Interlanguage(IL),cognitive approach are considered as three aspects closely related to Error Analysis (EA).Originated from CA, EA takes IL as its linguistic basis and cognitive approach as its psychological support.Comparing with CA, EA pays more attention to the learner himself rather than the linguistic forms, and error is therefore shifted from what should be avoided to the crucial approach to the exploration of the learner’s cognitive process.

Error Analysis and Error Correction in English Learning

As a field in applied linguistics, error analysis is not only an instrument for language acquisition research but also an auxiliary tool for language teaching. It plays a significant role both in exploring the learning rules and improving the teaching of foreign languages. With this information, teachers could adjust their teaching plan and make their teaching more effective. In this paper I present some common errors that my students make in their learning process. Based on these, a further elaboration on how to correct errors, including the attitudes to them, the integrative principles, the concrete methods and techniques for error correction is discussed as well.

Error-analysis与大学英语写作教学

非英语专业的大学英语写作教学一直是教学实践的一个重要环节,其目标是要在两年的基础学习阶段,迅速提高学生初步的书面表达思想能力。面对学生的英语水平、思维方式和性格特征等较大差异,教师在实际工作中对教学手段和内容应有不同的选择和侧重。通过以Error-analysis(错误分析研究)理论为基础开展教学实践活动,针对学生在写作中出现的某些语言方面的错误的具体问题,进行归纳和分析,然后进行系统的强化纠正训练。同时还总结了此活动对学生写作水平的正负面影响,以及学生对其心理的承受能力。

An Analysis of Errors in Second Language Acquisition

It is natural for language learner to make errors in the process of second language acquisition.But different linguists hold different views on the nature of errors and the methods of dealing with learner' s errors.Three main Western theories on errors are Contrastive Analysis,Error Analysis and the Interlanguage Theory.This paper first examines three most influential error theories,and then probes into their enlightenment on second language teaching.

Gross errors identification and correction of in-vehicle MEMS gyroscope based on time series analysis

This paper presents a novel approach to identify and correct the gross errors in the microelectromechanical system （MEMS） gyroscope used in ground vehicles by means of time series analysis. According to the characteristics of autocorrelation function （ACF） and partial autocorrelation function （PACF）, an autoregressive integrated moving average （ARIMA） model is roughly constructed. The rough model is optimized by combining with Akaike＇s information criterion （A/C）, and the parameters are estimated based on the least squares algorithm. After validation testing, the model is utilized to forecast the next output on the basis of the previous measurement. When the difference between the measurement and its prediction exceeds the defined threshold, the measurement is identified as a gross error and remedied by its prediction. A case study on the yaw rate is performed to illustrate the developed algorithm. Experimental results demonstrate that the proposed approach can effectively distinguish gross errors and make some reasonable remedies.

The Scientific Error Analysis在工程计算中的应用

介绍了TheScientificErrorAnalysis软件包的使用以及基于该软件包的误差分析的原理, 给出了工程计算实例。该软件包编程容易, 结果正确, 值得在工程误差分析中推广应用。