Accuracy Of Error Correction Research Articles

Computer-Assisted Online Learning of English Oral Pronunciation Based on DAE End-to-End Recurrent Neural Networks

With the development of globalization, learning a second language has received increasing attention from people. To improve English oral proficiency, a computer-aided online learning system for English oral pronunciation is studied. A denoising autoencoder is integrated into the system to create a simplified end-to-end recurrent neural network for pronunciation detection and diagnosis based on deep learning. The study first collected and preprocessed oral pronunciation data of English learners, including enhancing speech signals and reducing noise. Next, an RNN model with Long Short-Term Memory (LSTM) as the core was constructed to capture time series characteristics in pronunciation. And use DAE to extract features and reduce the influence of background noise to enhance the recognition of pronunciation features. At the same time, the study utilized web crawler technology to collect a large amount of oral pronunciation data from non-native English learners, and constructed an English oral corpus containing pronunciation errors. And in order to simulate real situations, white noise and pink noise were artificially added to the corpus in the study, and they were divided into training and testing sets in a ratio of 60% to 40%. The results showed that the classification accuracy of the system in the training and testing sets under white noise environment was 78.97% and 94.01%, respectively, and the classification accuracy in the pink noise environment was 76.19% and 94.03%, respectively. The system’s error detection accuracy in vowel and consonant pronunciation detection is 88.91% and 91.68%, respectively, and the error correction accuracy in vowel and consonant pronunciation detection is 90.67% and 91.96%, respectively. In summary, the research on computer-aided online learning of English oral pronunciation based on Denoising Auto Encoders end-to-end recurrent neural networks has effectively improved learning efficiency.

Research on business English grammar detection system based on LSTM model

Abstract In order to solve the problems that the current English grammar correction algorithms are not effective, the error correction ability is limited, and the error correction accuracy needs to be improved, this study proposes an automatic grammar correction method for business English writing based on two-way long short-term memory (LSTM) and N-gram. First, this study considers article and preposition errors as a special sequence labeling task, and proposes a Grammar error checking (GEC) method for sequence labeling based on bidirectional LSTM. During training, english as a second language (ESL) corpus and supplementary corpus are used to label specific articles or prepositions. Second, for noun simple-plural errors, verb form errors, and subject-verb inconsistency errors, a large number of news corpora are used to count the frequency of N-gram, and a GEC method based on ESL and news corpora N-gram voting strategy is proposed. Experimental results show that the overall F 1 value of the method designed in this study on the GEC data of CoNLL2013 is 33.87%, which is higher than the F 1 value of UIUC. The F 1 value of article error correction is 38.05%, and the F 1 value of preposition error correction is 28.89%. It is proved that this method can effectively improve the accuracy of grammar error correction and solve the gradient explosion problem of traditional error correction model, which is of great significance to further strengthen the practicality of automatic grammar error correction technology.

Transformer-customer relationship identification based on deep Gaussian mixture model in low-voltage distribution system

Accurate transformer-customer relationship is critical for better operation and management of low-voltage distribution system. It is of high cost to establish and check the profiles of transformer-customer relationship by manual maintenance, which is often mistakenly archived too. This paper proposes a novel automatic identification method based on a deep Gaussian mixture model with voltage data. The improved model is built with feature extraction network, clustering network and splitting and merging network, which can adjust the number of transformers adaptively and dynamically. It can also identify outliers of the data and effectively solve the uncertainty and imbalance of samples. Finally, the proposed method is tested in a practical power supply district which has 2752 customers with 9 transformers. Five different test cases are set to verify its effectiveness. The method can dynamically adjust to the actual number of transformer under different initial clustering numbers. The experimental results demonstrate that our model achieves an accuracy of up to 98.8% in profiling unlabeled users, while achieving an error correction accuracy of 99% in the case of imbalanced users in the transformer area. To further validate the superiority of our model, we conducted robustness experiments under different user scales and data loss scenarios. The results consistently outperform current state-of-the-art algorithms.

Decoding topological XYZ 2 codes with reinforcement learning based on attention mechanisms

Quantum error correction, a technique that relies on the principle of redundancy to encode logical information into additional qubits to better protect the system from noise, is necessary to design a viable quantum computer. For this new topological stabilizer code-XYZ 2 code defined on the cellular lattice, it is implemented on a hexagonal lattice of qubits and it encodes the logical qubits with the help of stabilizer measurements of weight six and weight two. However topological stabilizer codes in cellular lattice quantum systems suffer from the detrimental effects of noise due to interaction with the environment. Several decoding approaches have been proposed to address this problem. Here, we propose the use of a state-attention based reinforcement learning decoder to decode XYZ 2 codes, which enables the decoder to more accurately focus on the information related to the current decoding position, and the error correction accuracy of our reinforcement learning decoder model under the optimisation conditions can reach 83.27% under the depolarizing noise model, and we have measured thresholds of 0.18856 and 0.19043 for XYZ 2 codes at code spacing of 3–7 and 7–11, respectively. our study provides directions and ideas for applications of decoding schemes combining reinforcement learning attention mechanisms to other topological quantum error-correcting codes.

Image Identification and Error Correction Method for Test Report Based on Deep Reinforcement Learning and IoT Platform in Smart Laboratory

In order to solve the problems that most models are complex, time-consuming, and have difficulty in identifying image errors, an image identification and error correction method of test report based on deep reinforcement learning and the internet of things platform in the smart lab was proposed. Firstly, a smart lab architecture was designed based on the internet of things platform, achieving efficient operation of the laboratory through cloud edge collaboration. Then, the depth separable convolution improved convolutional neural network is used to extract image features, and the features are input into bidirectional recurrent neural networks (BiLSTM) for analysis to complete image recognition. Finally, the ICNN-BiLSTM model is used as the agent of reinforcement learning, and image error correction is completed by identifying the distance between the image and the key points of the reference image. Based on the Python platform, the proposed method was experimentally demonstrated, and the results showed that its average error correction accuracy reached 96.75%, with a processing time of 15.37s.

Research on Artificial Intelligence Assisted Decision Making System in Higher Level Sports Training

Abstract From the perspective of process monitoring of sports training, this paper discusses the basic framework and decision-making function system composition for establishing a decision-making system for higher vocational sports training. Integrate students’ sports data to design a real-time monitoring program for sports training intensity, which forms the mapping relationship between physical fitness and exercise intensity. Utilize and improve the Apriori algorithm to create an association rule mining model for the physical fitness of higher vocational students. Propose using the Multi-Agent model to optimize the data information management of the decision-making system and combine it with the RBF neural network algorithm for sports training performance prediction. To analyze the impact of the sports training decision-making system, application examples are introduced. Examine how association rules are implemented in the decision-making system of higher vocational sports training. When the number of frames is 300, the error correction accuracy before and after the improvement of the Apriori algorithm is 79.14% and 94.23%, respectively. The feedback rate of students’ performance information is maintained at about 0.85 with the assistance of the sports training decision-making system improved by the algorithm. The improved sports training decision-making system is more practical.

Do we learn from mistakes? The usefulness of examples of errors in online dictionaries

AbstractThe aim of this paper is to investigate the usefulness of examples that show typical learner errors in online pedagogical dictionaries of English for the accuracy of error correction as well as immediate and delayed retention of usage. The optimal positioning of examples of errors in entries is also researched. In an online experiment, participants did a sentence correction exercise with the help of purpose-built monolingual dictionary entries, where the provision and positioning of examples showing errors were controlled. Two test versions were created, which differed only in the presence of examples of errors in the entries. Usage retention was observed immediately after the test and two weeks later. The results indicate that it is worthwhile to include examples of errors in online learners’ dictionaries because they contribute greatly to the retention of usage in the long run. They also help to rectify errors, though the effect is not statistically significant. The positioning of examples showing errors in entries has no influence on error correction accuracy or usage retention. The study reveals examples of errors to be a valuable induction-oriented stand-alone dictionary component placed outside warning boxes, which typically include explicit grammar rules and promote deduction.

Wavelet transform-based characterization of printing ink penetration depth and image phase dissimilarity

Abstract In this paper, continuous wavelet transform and discrete wavelet transform are used to detect transient anomalies entrained in normal information and to demonstrate their components. Multi-scale analysis of wavelet transform, Haar wavelet basis and multi-scale edge detection algorithms are utilized to determine the modal extreme points and identify the edge points for faster and more accurate extraction of edge features of the image. In order to further validate the applicability and feasibility of wavelet transform for printing images and to determine the quality inspection criteria based on ink penetration depth and image phase anisotropy, MATLAB software is utilized to perform simulation tests. The results show that the wavelet transform can remove the noise generated by uneven illumination and printing background during the printing process and can detect the edges of the printing image with an error accuracy of ±0.063mm and meet the error correction accuracy of <0.4mm as required by the printing standard. The experiments verify the feasibility of the wavelet transform, which can characterize the depth of penetration of the printing ink and the image anisotropy and provides a theoretical basis for improving the quality of printing. The experiment confirms that wavelet transform can be used to measure printing ink penetration depth and image anisotropy, giving a theoretical basis for improving printing quality.

Essay auto-scoring using N-Gram and Jaro Winkler based Indonesian Typos

Writing errors on e-essay exams reduce scores. Thus, detecting and correcting errors automatically in writing answers is necessary. The implementation of Levenshtein Distance and N-Gram can detect writing errors. However, this process needed a long time because of the distance method used. Therefore, this research aims to hybrid Jaro Winker and N-Gram methods to detect and correct writing errors automatically. This process required preprocessing and finding the best word recommendations by the Jaro Winkler method, which refers to Kamus Besar Bahasa Indonesia (KBBI). The N-Gram method refers to the corpus. The final scoring used the Vector Space Model (VSM) method based on the similarity of words between the answer keys and the respondent’s answers. Datasets used 115 answers from 23 respondents with some writing errors. The results of Jaro Winkler and N-Gram methods are good in detecting and correcting Indonesian words with the accuracy of detection averages of 83.64% (minimum of 57.14% and maximum of 100.00%). In contrast, the error correction accuracy averages 78.44% (minimum of 40.00% and maximum of 100.00%). However, Natural Language Processing (NLP) needs to improve these results for word recommendations.

Determining quantum topological semion code decoder performance and error correction effectiveness with reinforcement learning

Quantum error correction technology is a vital method to eliminate noise during the operation of quantum computers. To solve the problem caused by noise, in this paper, reinforcement learning is used to encode defects of Semion codes, and the experience replay technique is used to realize the design of decoder. Semion codes are quantum topological error correction codes with the same symmetry group Z2 as Kitaev toric codes, we used the topological characteristics of error correction codes to map qubits to multi-dimensional space, and error correction accuracy of the decoder is calculated to be 77.5%. Calculate the threshold of topological quantum Semion code, depending on the code distance, resulting in different thresholds, pthreshold = 0.081574 when the code distance is d = 3, 5, 7 and threshold pthreshold = 0.09542 when the code distance is d = 5, 7, 9. And we design the Q-network to optimize the cost of quantum circuit gates and compare the size of the cost reduction under different thresholds. Reinforcement learning is an important method for designing Semion code decoders and optimizing numerical values, providing more general error models and error correction codes for future machine engineering decoders.

Based on Quantum Topological Stabilizer Color Code Morphism Neural Network Decoder

Solving for quantum error correction remains one of the key challenges of quantum computing. Traditional decoding methods are limited by computing power and data scale, which restrict the decoding efficiency of color codes. There are many decoding methods that have been suggested to solve this problem. Machine learning is considered one of the most suitable solutions for decoding task of color code. We project the color code onto the surface code, use the deep Q network to iteratively train the decoding process of the color code and obtain the relationship between the inversion error rate and the logical error rate of the trained model and the performance of error correction. Our results show that through unsupervised learning, when iterative training is at least 300 times, a self-trained model can improve the error correction accuracy to 96.5%, and the error correction speed is about 13.8% higher than that of the traditional algorithm. We numerically show that our decoding method can achieve a fast prediction speed after training and a better error correction threshold.

Grammar System of TCFL Driven by Neural Network Technology.

With the economy's continued and stable growth, China's political and economic influence in the international community has grown, and more and more friends from all over the world are requesting to learn Chinese and visit China. The growth of information technology and curriculum integration has had a significant impact on TCFL (teaching Chinese as a foreign language). Facing the new situation will enable us to gain a fresh perspective on the current state of TCFL grammar system research. Through specific teaching practice, this paper verifies the effectiveness of teaching Chinese as a foreign language and cultural vocabulary. This paper proposes a grammar error correction scheme based on hybrid models—Transformer model and N-gram model—that dynamically combine the outputs of different neural modules to improve the model's ability to capture semantic information, with the goal of correcting Chinese grammar errors. Experiments show that the Transformer and N-gram model-based Chinese grammar error correction strategy performs well in the global effect, and the overall performance is the best in the detection and positioning levels. At the detection level, the model in this document has the highest error correction accuracy of 0.64 and the highest recall rate of 0.67. The results show that adding an attention mechanism to a grammatical error correction model can improve its computational efficiency.

Piano Music Teaching under the Background of Artificial Intelligence

The work is aimed at solving the problems of easy trapping into local extremes and slow convergence speed of the traditional music teaching evaluation system on Backpropagation Neural Network (BPNN). The traditional note recognition methods are susceptible to high noise complexity. Firstly, the Levenberg Marquardt (LM) algorithm is used to optimize the BPNN; secondly, an improved endpoint detection algorithm is proposed by short-term energy difference, which can accurately identify the time value of each note in the piano playing audio. By the traditional frequency domain analysis method, a radical frequency extraction algorithm is proposed by the improved standard harmonic method, which can accurately identify the note’s pitch. Finally, a piano performance evaluation model by BPNN is implemented, and the model is implemented by the Musical Instrument Digital Interface (MIDI) system. This evaluation model can be used to correct the errors of students’ performances in the piano music teaching process and to perform overall evaluation, rhythm evaluation, and expressive evaluation. Teachers and students play minuet to collect experimental samples to train BPNN and test the performance of the evaluation model. The practical result shows that (1) after 3000 times of training, the neural network error is less than 0.01, and the network converges; (2) the evaluation results of the piano performance evaluation model designed are basically in line with the actual level of the performer and have specific feasibility; and (3) the optimized BPNN is used to correct errors during performances with an accuracy rate of 94.3%, which is 5.25% higher than the traditional method. The error correction accuracy rate for pitch is 92.9%, which is 5.21% higher than the traditional method. The optimized BPNN has significantly improved the error correction accuracy of the notes and pitches played by the player. The model can effectively help piano beginners correct errors and improve the accuracy and efficiency of the practice. The purpose of this study is to alleviate the scarcity of piano teachers, reduce the work intensity of piano teachers, realize automatic error correction and objective evaluation of playing, and provide necessary technical support for improving the efficiency of piano music teaching.

Analysis of Psychological Shaping Function of Music Education under the Background of Artificial Intelligence.

In order to solve the problem of integrating intelligent technology into music teaching, this paper puts forward the methods of using intelligent technology to optimize the music teaching system; to enhance the effectiveness of music psychological guidance, music intelligent creation, the development of Yi Guzheng platform, the integration of online sparring technology, and the rational use of Mu class platform, and to ensure the construction of loop curriculum system with “intelligent piano.” RBF algorithm has strong data processing ability, which can ensure the operation quality of music score, music score and performance learning modules, and effectively strengthen the training effect of music psychological function. The artificial intelligence platform is more progressive in evaluation. It can use emotion to evaluate courses, grasp the course direction in advance, ensure the construction quality of psychological function, and improve the effect of music teaching; take 100 people as the basis of students, and measure the time proportion of students' music learning in the intelligent system. cjσj refers to the learning situation of music courses within the learning time X of students with excellent psychological quality of music. This result is parallel. Y represents the best score of music output by each algorithm. 2024 music intelligent platforms have collected various course resources with difficulty coefficients of 1 to 5, and the course resources of pictures and videos are sufficient. It can provide students with comprehensive music psychological education and give full play to the teaching advantages of intelligent technology. It can be seen from the data: the operation of music score, music score and performance module viewed in March 2021, with high transportation times and resource download times, indicating that the operation is in good condition; The error correction accuracy of the system is greater than 99%, indicating that the system has strong error correction ability.

Mining statistically-solid k-mers for accurate NGS error correction

BackgroundNGS data contains many machine-induced errors. The most advanced methods for the error correction heavily depend on the selection of solid k-mers. A solid k-mer is a k-mer frequently occurring in NGS reads. The other k-mers are called weak k-mers. A solid k-mer does not likely contain errors, while a weak k-mer most likely contains errors. An intensively investigated problem is to find a good frequency cutoff f0 to balance the numbers of solid and weak k-mers. Once the cutoff is determined, a more challenging but less-studied problem is to: (i) remove a small subset of solid k-mers that are likely to contain errors, and (ii) add a small subset of weak k-mers, that are likely to contain no errors, into the remaining set of solid k-mers. Identification of these two subsets of k-mers can improve the correction performance.ResultsWe propose to use a Gamma distribution to model the frequencies of erroneous k-mers and a mixture of Gaussian distributions to model correct k-mers, and combine them to determine f0. To identify the two special subsets of k-mers, we use the z-score of k-mers which measures the number of standard deviations a k-mer’s frequency is from the mean. Then these statistically-solid k-mers are used to construct a Bloom filter for error correction. Our method is markedly superior to the state-of-art methods, tested on both real and synthetic NGS data sets.ConclusionThe z-score is adequate to distinguish solid k-mers from weak k-mers, particularly useful for pinpointing out solid k-mers having very low frequency. Applying z-score on k-mer can markedly improve the error correction accuracy.

Pluribus-Exploring the Limits of Error Correction Using a Suffix Tree.

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A New Error Correction Algorithm for Chinese Placename

In order to improve the accuracy of error correction for Chinese placename, the cleansing framework based on the address-name index table and bi-direction placename error correction method are proposed. Firstly, the error correction algorithms in data cleansing area are reviewed. Secondly, the cleansing framework based on the address-name index table is proposed for the features of Chinese placename and the low accuracy of existing algorithms. In this framework, the structure of address-name index table and relevant concepts are defined, and a new bi-direction placename error correction method is used to improve the accuracy of algorithm by using the address-name index table. In this method, the concepts of partial similarity and whole similarity are introduced, and Chinese placename are matched and corrected in forward firstly, then they are searched from address-name index table and corrected in reverse. Finally, extensive simulation experiments are conducted to prove the feasibility and rationality of method, the results of which show that the bi-direction placename error correction method is better than the others in the performance metrics of implementation precision ratio and recall ratio for error correction of Chinese placename.

An ameliorative algorithm of two-dimensional Poisson equation based on genetic parallel successive over-relaxation method

There exist some disadvantages in the calculation of two-dimensional Poisson equation with several common methods. A new ameliorative algorithm is presented. It is based on a parallel successive over-relaxation (PSOR) method, by using the multi-objective genetic algorithm to search for optimal relaxation factor, with which the problem of optimal relaxation factor selection in PSOR is solved. The multi-objective fitness function is constructed, with which the genetic algorithm parameters are optimized. The analysis mainly focuses on algorithm computation, time cost and accuracy of error correction. The performance of the ameliorative algorithm is compared with those of Jacobi, Gauss-Seidel, Successive over relaxation iteration (SOR) and PSOR. Experimental results show that relaxation factor has a significant effect on the speed of solving Poisson equation, as well as the accuracy. The improved algorithm can increase the speed of iteration and obtain higher accuracy than traditional algorithm. It is suited for solving complicated finite difference time domain equations which need high accuracy. The higher the accuracy requirement, the better the performance of the algorithm is and the more computation time can also be saved.

The Effects of a Fluency Training Package on Paraprofessionals’ Presentation of a Reading Intervention

Paraprofessionals are widely employed in response to intervention (RTI) settings to provide instruction to students at-risk for reading disabilities. However, little research has addressed effective and efficient ways to train these paraprofessionals to deliver instruction with high fidelity. In addition, given the limited time and finances available in most districts, training needs to be as efficient as possible. This study assessed the effects of a 5-hour fluency training package on the presentation rates, praise rates, and error correction accuracy of five paraprofessionals providing supplemental reading instruction within an RTI system using a multiple baseline design across participants. Students’ reading accuracy and percentage of intervals with on-task behavior were evaluated. Paraprofessionals generally increased their presentation rates, praise rates, and error correction accuracy. Students’ behaviors were affected less.

Coordinating sentence composition with error correction: A multilevel analysis

Error analysis involves detecting and correcting discrepancies between the ‘text produced so far’ (TPSF) and the writer’s mental representation of what the text should be. While many factors determine the choice of strategy, cognitive effort is a major contributor to this choice. This research shows how cognitive effort during error analysis affects strategy choice and success as measured by a series of online text production measures. We hypothesize that error correction with speech recognition software differs from error correction with keyboard for two reasons. Speech produces auditory commands and, consequently, different error types. The study reported on here measured the effects of (1) mode of presentation (auditory or visualtactile), (2) error span, whether the error spans more or less than two characters, and (3) lexicality, whether the text error comprises an existing word. A multilevel analysis was conducted to take into account the hierarchical nature of these data. For each variable (interference reaction time, preparation time, production time, immediacy of error correction, and accuracy of error correction), multilevel regression models are presented. As such, we take into account possible disturbing person characteristics while testing the effect of the different conditions and error types at the sentence level. The results show that writers delay error correction more often when the TPSF is read out aloud first. The auditory property of speech seems to free resources for the primary task of writing, i.e. text production. Moreover, the results show that large errors in the TPSF require more cognitive effort, and are solved with a higher accuracy than small errors. The latter also holds for the correction of small errors that result in non-existing words.