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Combined with the real-time and robust requirements of paper detection in the common paper-pen interaction, a fast paper detection method based on edge detection is proposed. In the edge detection stage, a fast paper edge detection method based on cross-layer feature fusion is advanced. The linear bottleneck inverted residual blocks and efficient channel B-ECA blocks are added to the HED backbone, which greatly reduce the numbers of parameters and increase the weight of salient features. The features of all stages and all layers are fused in order to retain the more edge features. The high-level features are upsampled and cross-layer fused with the low-level features to solve the problem of edge blur. Training and testing on the self-made MPDS data set shows that, compared with the original HED method, the proposal increases the ODS and OIS by 8.1% and 6.6% respectively, and improves the detection speed from 22.08 FPs to 39.02 FPS. In the paper extraction stage, a paper extraction method based on the paper structure is proposed. After thinning the paper edge based on non-maximum suppression, detection and filtering the line, and extraction paper vertex based on structural constraints, the image containing only paper is extracted. The experimental results show that the paper extraction method can quickly and accurately extract the entire paper image in various complex desktop environments and occlusion situations, which provides an interaction basis for the common paper-pen interaction method. [ABSTRACT FROM AUTHOR]

Aiming at low success rate and efficiency of exam paper generation for small exam data- base, the paper proposed a prediction calculation based intelligent exam paper generation algorithm without backtracking. Based on prediction calculation, according to the constraints, this algorithm firstly figures out the allocation matrix that meets the requirements of the exam database , and then accesses the exam database. This can improve the success rate of exam paper generation and there is no backtracking when the exam database is accessed. Besides , according some strategy , a batch extraction scheme is used to speed up exam paper generation. This algorithm has a certain universality and high research value for constructing intelligent exam system. [ABSTRACT FROM AUTHOR]

In order to improve the fitness of RMB paper currency number recognition algorithm, based on structural feature of characters, a novel RMB paper currency number recognition algorithm is proposed, which is based on multiple mutual compensation structure characteristics. And the image pre process of RMB paper currency number is studied including image positioning, image binarization, single character segmentation, character size normalization, etc. Algorithms of English letter arrangement, Arabia digital arrangement and their mixed arrangement of RMB paper currency number are designed. These algorithms are introduced by taking partial flow chain of mixed arrangement recognition program as an example. An ARM embedded system is used to validate three recogntion programs. Some suggestions are proposed to solve the issues such as font diference of characters, and character interference in the experiment. The statistical results of accuracy of three recognition programs show that the algorithm has high precision and meets the practical needs. [ABSTRACT FROM AUTHOR]

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Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aiming at the problems of small chaotic space and weak chaotic ability of existing chaotic mapping models, a new three-dimensional chaotic mapping model is designed by coupling two-dimensional Henon chaotic mapping model and Sine chaotic mapping model. Aiming at the problem that the security of encryption system will be reduced if the key is reused, this paper designs a new key generation method using Chebyshev mapping and plaintext image. Using the 3D chaotic mapping and key generation method designed in this paper, a color image encryption method is proposed. The results show that the method can encrypt images safely and effectively and protect the security of image information. [ABSTRACT FROM AUTHOR]

In the era of big data, with the continuous popularization of social media, various text data are growing in the network and in life. It is of great significance to analyze and manage text data using text classification technology. Text classification is a basic research field in the field of artificial intelligence natural language processing. Under the given criteria, it classifies text according to content. The application scenarios of text classification are very extensive, such as sentiment analysis, topic classification, relationship classification, etc. Deep learning is a method of representation learning based on data in machine learning, and it shows good classification effect in text data processing. Chinese text and English text have differences in form, sound, and image. Focusing on the uniqueness of Chinese text classification, this paper analyzes and expounds the deep learning methods used for Chinese text classification, and finally sorts out commonly used datasets for Chinese text classification. [ABSTRACT FROM AUTHOR]

Code summarization can help developers understand the function and implementation of the code. The code summarization generation model can automatically identify the key information in the code and generate relevant summarization to improve the readability and maintainability of the code. Existing code summarization generation models usually only use abstract syntax tree structure information to represent code, resulting in low-quality mode--generated summarization. Aiming at this problem, this paper proposes a code summarization generation model that integrates multi-structure data. Firstly, the model adds data flow graph structure information to represent code on the basis of abstract syntax tree. Secondly, in order to capture the global information of the code, the model uses Transformers encoder to encode the abstract syntax tree sequence. In addition, the model uses the graph neural network to extract features from the data flow graph and provide information such as the computational dependences between variables. Finally, the model uses the cross-modal attention mechanism to fuse the two features of the abstract syntax tree and the data flow and generate corresponding summarization through the Transformer decoder. The experimental results show that, compared with the six mainstream models, the model improves the scores of BLEU, METEOR and ROUGEL on the Java and Python data-sets, and the generated summarization is also very readable. [ABSTRACT FROM AUTHOR]

In the era of big data, the demand for high-density and large-capacity storage technology is increasing day by day. Unlike traditional storage technologies that record data bit by bit, holographic storage uses two-dimensional data pages as the link for reading and writing, adopting a three-dimensional volume storage mode. With advantages such as high storage density, fast data conversion rate, energy efficiency, safety, and ultra-long-term preservation, holographic storage is expected to become the strong competitor for mass cold data storage. This paper focuses on phase-modulated collinear holographic storage and analyzes the current research status of error correction codes for holographic storage. A detailed introduction is provided to an reference beam-assisted low-density parity check (LDPC) code scheme. [ABSTRACT FROM AUTHOR]

In industrial production, defects are treated differently based on their severity, so it is necessary to classify defects. However, in actual production, the classification accuracy is often insufficient due to the presence of few easily confused defects, which requires conservative treatment of all defects in production practice, resulting in significant human and economic costs. To solve this problem, this paper proposes a method for interactive separating easily confused defects. This method separates few easily confused defects from other defects, ensuring that the classification results of the remaining majority of defects can be directly used. This method selects easily confused defects from the training data as one or more new defect categories, called virtual defects, so that the trained network can distinguish between virtual defects and other defects. This paper designs a visual interface to assist users in interactively selecting easily confused defects to construct virtual categories. CMOS defect data from actual industrial sites are adopted for effectiveness verification, and the results show that the proposed method can quickly classify few confusing defects and ensure that the classification accuracy of remaining defects meets the requirements of industrial applications. [ABSTRACT FROM AUTHOR]

Satisfiability modulo theories (SMT) refers to the decidability problem of first-order logic formulas under specific background theories. SMT based on first-order logic have a stronger expressive capability compared to SAT, with higher abstraction ability to handle more complex issues. SMT solvers find applications in various domains and have become essential engines for formal verification. Currently, SMT is widely used in fields such as artificial intelligence, hardware RTL verification, automated reasoning, and software engineering. Based on recent developments in SMT, this paper first expounds on the fundamental knowledge of SMT and lists common background theories. It then analyzes and summarizes the implementation processes of Eager, Lazy, and DPLL(T) methods, providing further insights into the implementation processes of mainstream solvers Z3, CVC5, and MathSAT5. Subsequently, the paper introduces extension problems of the SMT as #SMT, the SMTlayer approach applied to deep neural networks (DNNs), and quantum SMT solvers. Finally, the paper offers a per spective on the development of SMT and discusses the challenges they face. [ABSTRACT FROM AUTHOR]

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Convolutional neural networks have achieved great success in the field of image analysis. With the development of deep learning, deep learning models are becoming more and more complex, and he amount of deep learning calculations is increasing rapidly. The sparse algorithm can effectively reduce the amount of deep learning calculations without reducing the accuracy. This paper uses three different pruning strategies under the ResNet18 model and GoogleNet model to reduce the calculation amount of the deep learning model. The results show that the global unstructured pruning strategy has a sparsity of 94% and 90% without reducing the accuracy respectively, the level unstructured pruning strategy has an average sparsity of 83% and 56% without basically reducing the accuracy respectively, and the level structured strategy has an average sparsity of 34% and 22% without basically reducing the accuracy respectively. Under the three pruning strategies, the delay and power consumption results obtained by running the sparse deep learning model in the Eyeriss deep learning accelerator shows that, compared with the unpruned strategy, under the ResNet model, the global unstructured pruning strategy has a 66.0% reduction in latency and a 60.7% reduction in power consumption, the level unstructured pruning strategy has a 66.0% reduction in delay and a 80.6% reduction in power consumption, and the level structured pruning strategy has a 65.6% reduction in latency and a 33.5% reduction in power consumption. Under the GoogleNet model, the global unstructured pruning strategy has a 4.5% reduction in latency and a 63.2% reduction in power consumption, the level unstructured pruning strategy has a 73.6% reduction in delay and a 55.0% reduction in power consumption, and the level structured pruning strategy has a 26.8% reduction in latency and a 5.8% reduction in power consumption. Therefore, this paper concludes that the global unstructured pruning strategy can greatly reduce he delay and energy consumption without reducing the accuracy. Under the level unstructured pruning strategy, the delay and energy consumption can be greatly reduced under the premise of slightly reducing the accuracy. [ABSTRACT FROM AUTHOR]

With the development of artificial intelligence, digital images have been widely used in various fields. However, due to the appearance of image editing software, a large number of images have been tampered with maliciously, which seriously affects the authenticity of image content. Different from the general object detection, the study of image tampering detection needs to pay more attention to the tamper information of the image itself, which is often manifested in a weak form. Therefore, the detection model needs to focus on learning more abundant tamper features. This paper proposes a dual-stream Faster R-CNN model that combines gradient edge information and attention mechanism, and the model can realize detection and location of regions with different tampering types. One of the two streams is the color stream, which uses the attention mechanism to extract the surface features of the image, such as brightness contrast, visual difference of tampering with the boundary, etc. The second of the two streams is a gradient stream. A Gradient high-pass filter is used to enhance the anomaly edge features between the real area and the tampered area, making it easier for the model to find faint tampered traces in the tampered image. Finally, the features of color stream and gradient stream are fused by means of compact bilinear pooling. Due to the relatively small size of publicly available image tampering data sets, the Pascal VOC 2012 is used to create an image tampering detection data set which containing 10 010 images for model pre-training. The experimental results on COVER, Columbia, and CASIA data sets show that the model proposed in this paper improves the detection accuracy by 7.1% to 9.6% compared to the latest models, and exhibits higher robustness under JPEG compression and image blur attacks. [ABSTRACT FROM AUTHOR]

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Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Electromagnetic method of geoelectrical frequency domain has a wide range of applications in exploring Earth's deep structure, petroleum exploration, environmental and engineering surveying, and its accuracy and efficiency of numerical simulation directly affect the interpretation results of data. However, there are currently issues with low accuracy and long computation time in three-dimensional numerical simulation of frequency domain electromagnetic fields, as well as limitations in computational scale. This paper proposes to use integral equation method and direct solution method to improve the solution accuracy, and adopt technologies such as hybrid parallel processing at multiple levels and multiple grain sizes, distributed storage, etc., to greatly reduce the computation time and expand the computational scale. This paper implements a fast, high-precision, and highly scalable three-dimensional numerical simulation method of frequency domain electromagnetic fields, which includes parallel processing between frequency points, parallel filling of impedance matrix, and parallel direct solution of equations. Firstly, the theoretical framework of integral equation method and its parallel implementation solution are introduced in detail. Then, typical cases are selected to verify the correctness of the program by comparing with previous calculation results. Finally, the scalability is tested for a large-scale example with 16 frequencies, 16×12 495 unknowns, and 861 observation points. Compared to a single node with 32 processes, when the computational scale reaches 256 nodes and 8 192 processes, the speedup ratio is 69.69 and the parallel efficiency is 27.22%. This large-scale parallel algorithm is applicable to both geomagnetic and controlled source audio geomagnetic integral equation methods. [ABSTRACT FROM AUTHOR]

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Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Currently, mainstream quantum computing simulators are generally based on numerical calculations, which suffer from precision loss, lack of intuitive representation of quantum states, and difficulty in supporting parameterized quantum circuits. Although symbolic quantum computing simulators have been proposed, they are limited in describing parameterized quantum states, constructing custom quantum gates, and integrating with quantum programming environments. To address this issue, this paper proposes a symbolic quantum computing simulator, SymQC. SymQC can use either full amplitude vectors or Dirac symbols to represent quantum states, calculate the equivalent matrix of symbolically parameterized quantum circuits, simulate the evolution of quantum states under quantum circuits, and output the execution results of quantum algorithms in different forms. This paper describes the software structure of SymQC and provides a mathematical proof of a commonly used quantum state update algorithm. Algorithm instances including variational quantum eigensolver (VQE) algorithm are used to verify the ability of SymQC. [ABSTRACT FROM AUTHOR]

In response to the overall trend and practical application of multi-threshold points in electromechanical equipment fault data detection, this paper proposes a cloud-edge collaborative electromechanical equipment fault detection method based on a variational autoencoder with gated recurrent unit (VAE-GRU). A cloud-edge collaborative electromechanical equipment fault detection architecture is structed, including a terminal equipment layer, an edge node layer, and a cloud center layer, in which electromechanical equipment is detected for faults through collaboration between the cloud center and edge nodes. The VAE-GRU model is design, where the input data is sampled by VAE, and GRU is used to capture the long-term correlation of the timing data. A dynamic threshold selection algorithm is used to calculate the fault detection threshold, that can automatically select the optimal threshold for different data sets to improve fault detection accuracy. Experimental results show that the proposed method improves the accuracy of electromechanical equipment fault detection while reducing latency, enduring the normal and stable operation of electromechanical equipment. [ABSTRACT FROM AUTHOR]

To meet the fast iteration speed and high computing power requirements of deep learning models, mainstream hardware vendors are increasingly inclined towards heterogeneous systems consisting of general-purpose processors and AI-specific accelerator cores. However, AI-specific accelerator ores only support certain core operators and do not have general programming capabilities. Therefore, how to efficiently deploy deep learning tasks on such heterogeneous architectures is worth further research. Based on the domestically developed FT-Matrix DSP+MatrixZone heterogeneous system platform, this paper designs and implements a deep learning programming framework, called KaiSa. KaiSa analyzes the input parameters of the deep learning model, identifies the operator type, and assigns it to the corresponding computing core. For complex operators, KaiSa automatically completes the optimal search for the block size based on a performance model, improving the performance of dual-core parallel computing. At the same time, KaiSa shields all low-level hardware details to provide users with a friendly programming environment for efficient program development. Experimental results show that KaiSa can achieve performance improvements of up to 39.0%. [ABSTRACT FROM AUTHOR]

High speed serial interface is the key intellectual property (IP) for the inter-chip interconnection during the high performance computers and data centers. As the single-channel rate of the serial interface evolves from 56 Gbps to 112 Gbps, the high speed serial interface faces a sharp increase in the bit error rate (BER), which seriously affects the interconnection performance and the system stability. In order to solve the problem of the high bit error rate at 112 Gbps PAM4 receiver, a cooperative adaptive equalizer is proposed in this paper. Firstly, an adaptive cooperative equalization algorithm for three kinds of equalizers is proposed to achieve low bit error under the condition of large insertion losses. Then the blind adaptive equalization algorithm based on decision feedback equalizer is proposed to shorten the link training time and reduce the hardware overhead. This paper completes the circuit implementation of the receiver with the cooperative adaptive equalizer under the 12 nm CMOS technology. The simulation results show that the receiver with the proposed cooperative adaptive equalizer can steadily receive the 112 Gbps PAM4 signal under 36.5 dB condition with the BER less than 1e-12. It also can achieve the convergence period of about 400 ns and a power consumption increase of only about 2.3%. [ABSTRACT FROM AUTHOR]

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Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The lack of large parallel corpora is one of the key issues in low-resource neural machine translation. This paper proposes a neural machine translation method based on language model distillation, which regularizes neural machine translation training by using a monolingual language model. This method introduces prior knowledge contained in the language model to improve translation results. Specifically, we draw on the idea of knowledge distillation, and use the target-side language model (teacher model) trained on rich monolingual data to construct the regularization factor of the low-resource neural machine translation model (student model), allowing the translation model to learn highly generalized prior knowledge from the language model. Unlike traditional monolingual language models that participate in the decoding process, the language model in this method is only used during training and does not participate in the inference stage, so it can effectively improve decoding speed. Experimental results on two low-resource translation datasets of Uyghur-Chinese and Tibetan-Chinese from the 17th national machine translation conference (CCMT2021) show that compared with the current state-of-the-art language model fusion baseline system, BLEU can be improved by 1. 42 points (Tibetan-Chinese) to 2.11 points (Chinese-Uyghu). [ABSTRACT FROM AUTHOR]