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5th International Conference on Communications, information System and Computer engineering, CISCE 2023

作者： Qin, Lu Shi, Lin Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming China

ISBN: (纸本)9798350326796

Chinese character skeletons are key representations which abstract structures of Chinese characters into skeletons. Extraction the skeleton from images of Chinese characters is a fundamental task in field of Chinese character process. Existing image skeleton thinning algorithms for extracting Chinese character skeletons produced bifurcations which could not accurately reflect topological features and writing trajectories of Chinese characters. Existing image skeleton extraction methods based on deep neural networks also had difficulty in obtaining accurate single-pixel Chinese character skeletons. Here we proposed a hybrid Chinese character skeleton extraction method to resolve the above problems. Firstly, a dynamic skeleton extraction algorithm was proposed to automatically obtain a dataset using a customized digitalization platform of Chinese handwriting process. Secondly, an improved linear span network was trained using the dataset to extract the Chinese character skeletons. Thirdly, the extracted skeletons were refined using an image-thinning algorithm to obtain single-pixel Chinese character skeletons. Results showed that our method not only preserved the topological structures and writing trajectories of Chinese characters but also maintained connectivity at intersections of strokes which avoided generating burrs and bifurcations. © 2023 IEEE.

关键词： Extraction

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Investigation to Screen Region Division for Stable Input of Eye-controlled Cursor

Investigation to Screen Region Division for Stable Input of ...

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2023 IEEE International Conference on Control, Electronics and Computer technology, ICCECT 2023

作者： Chen, Cheng Yin, Jibin Fu, Tiewei Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming China

ISBN: (纸本)9798350399028

Aiming at the problems of stable cursor hovering and "Midas Touch"that are difficult to achieve in the current eye-controlled cursor technology, this paper has carried out an experimental study on the stable input of the eye-controlled cursor: Firstly, divide the screen into several small rectangular areas. When the eye-controlled cursor appears in any rectangular region, the position of the cursor will be fixed at the center of the rectangular region, and there will be no shaking of the eye-controlled cursor in a small range due to the natural wobble of the eyeball pupil. Then, the screen interface UI and icon sizes are adjusted according to the screen division level, to ensure that there is only one click target in each small rectangular area of the screen, and to ensure that there is no ambiguity when clicking on the eye-controlled cursor. Finally, by combining the task completion time, task completion rate, error rate, task difficulty, performance indicators, user satisfaction and other test data information to determine the most appropriate screen division size. © 2023 IEEE.

关键词： Touch screens

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Thai-English Neural Machine Translation Method with Local and Global Syllable Feature 27

Thai-English Neural Machine Translation Method with Local an...

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27th International Conference on Asian Language Processing, IALP 2023

作者： Xiong, Ming Liu, Ruimin Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming China

ISBN: (纸本)9798350330786

Neural Machine Translation (NMT) has superseded Statistical Machine Translation (SMT) owing to the advent of deep learning in natural language processing. However, enhancing the quality of low-resource NMT involves more nuanced strategies beyond data-dependent methods, especially for languages like Thai that lack substantial parallel corpus data for Thai-English translation. Addressing this problem, we introduce a Thai-English NMT methodology that amalgamates local and global syllabic features. Our approach employs a CNN-based module for extracting local syllable characteristics and capturing interdependencies among Thai syllable characters. Concurrently, a global syllable feature extraction mechanism is used to preserve sentence-level syllable traits. The integration of these features significantly augments Thai syllable context representation. In experiments conducted on the Thai-English corpus from the AL T dataset, our method improved the BLEU score by 2.11 points above the baseline, achieving a score of 12.99. This research holds potential implications for the translation of other syllable-based, low-resource languages, as well. © 2023 IEEE.

关键词： Neural machine translation

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Phase Demodulation under Free Undersampling in Heterodyne Coherent Φ-OTDR 5

Phase Demodulation under Free Undersampling in Heterodyne Co...

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5th International Conference on Intelligent Control, Measurement and Signal Processing, ICMSP 2023

作者： Cui, Maojiang Li, Chuan Zhao, Zhengang Qian, Heng Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming China

ISBN: (纸本)9798350336030

Heterodyne coherent detection phase-sensitive optical time-domain reflectometer (Φ-OTDR) is widely used in structural health monitoring of large facilities due to the ease of achieving stable phase demodulation, but it usually requires a high sampling rate. The sampling rate can be reduced effectively by undersampling, but the sampling rate of undersampling is strictly limited by spectrum aliasing effect. To solve this problem, this paper proposes a free undersampling phase demodulation method in the heterodyne coherent Φ-OTDR. The method changes from traditional 'distance' direction demodulation to 'time' direction demodulation of the recombination signal. Therefore, the spectrum aliasing in the 'distance' direction caused by undersampling does not affect the demodulation in the 'time' direction, and the sampling rate of undersampling can be freely selected. In the experiment, we proposed method can accurately demodulate the phase in sampling rates of 100 MSa/s and 10 MSa/s that within the sample rate restricted area of conventional undersampling demodulation method. © 2023 IEEE.

关键词： Demodulation

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Feature selection of BOF steelmaking process data by using an improved grey wolf optimizer

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Journal of Iron and Steel Research International 2022年第8期29卷 1205-1223页

作者： Zong-xin Chen Hui Liu Long Qi Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming 650500YunnanChina Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and TechnologyKunming 650500YunnanChina

Basic oxygen furnace(BOF)steelmaking end-point control using soft measurement models has essential value for economy and ***,the high-dimensional and redundant data of the BOF collected by the sensors will hinder the performance of *** traditional feature selection results based on meta-heuristic algorithms cannot meet the stability of actual industrial *** order to eliminate the negative impact of feature selection application in the BOF steelmaking,an improved grey wolf optimizer(IGWO)for feature selection was proposed,and it was applied to the BOF data ***,the proposed algorithm preset the size of the feature subset based on the new encoding scheme,rather than the traditional uncertain number ***,opposition-based learning was used to initialize the grey wolf population so that the initial population was closer to the potential optimal *** addition,a novel population update method retained the features closely related to the best three grey wolves and probabilistically updated irrelevant features through measurement or random *** methods were used to search feature subsets to maximize search capability and stability of algorithm on BOF steelmaking ***,the proposed algorithm was compared with other feature selection algorithms on the BOF data *** results show that the proposed IGWO can stably select the feature subsets that are conductive to the end-point regression accuracy control of BOF temperature and carbon content,which can improve the performance of the BOF steelmaking.

关键词： Basic oxygen furnace Feature selection Grey wolf optimization Regression Opposition-based learning

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Positron Emission Tomography Lung Image Respiratory Motion Correcting with Equivariant Transformer

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Computers, Materials & Continua 2024年第5期79卷 3355-3372页

作者： Jianfeng He Haowei Ye Jie Ning Hui Zhou Bo She Faculty of Information Engineering and Automation Kunming University of Science and TechnologyYunnan Key Laboratory of Artificial IntelligenceKunming650500China School of Physics and Electronic Engineering Yuxi Normal UniversityYuxi653100China PET/CT Center Affiliated Hospital of Kunming University of Science and TechnologyFirst People’s Hospital of Yunnan ProvinceKunming650031China

In addressing the challenge of motion artifacts in Positron Emission Tomography (PET) lung scans, our studyintroduces the Triple Equivariant Motion Transformer (TEMT), an innovative, unsupervised, deep-learningbasedframework for efficient respiratory motion correction in PET imaging. Unlike traditional techniques,which segment PET data into bins throughout a respiratory cycle and often face issues such as inefficiency andoveremphasis on certain artifacts, TEMT employs Convolutional Neural Networks (CNNs) for effective featureextraction and motion ***’s unique approach involves transforming motion sequences into Liegroup domains to highlight fundamental motion patterns, coupled with employing competitive weighting forprecise target deformation field generation. Our empirical evaluations confirm TEMT’s superior performancein handling diverse PET lung datasets compared to existing image registration networks. Experimental resultsdemonstrate that TEMT achieved Dice indices of 91.40%, 85.41%, 79.78%, and 72.16% on simulated geometricphantom data, lung voxel phantom data, cardiopulmonary voxel phantom data, and clinical data, respectively. Tofacilitate further research and practical application, the TEMT framework, along with its implementation detailsand part of the simulation data, is made publicly accessible at https://***/yehaowei/temt.

关键词： PET lung scans respiratory motion correction triple equivariant motion transformer lie group motion decomposition

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Chinese Grammatical Error Correction via Large Language Model Guided Optimization Training 23rd

Chinese Grammatical Error Correction via Large Language Mod...

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23rd China National Conference on Computational Linguistics, CCL 2024

作者： Liu, Xiao Li, Ying Yu, Zhengtao Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China Yunnan Key Laboratory of Artificial Intelligence Kunming China

ISBN: (纸本)9789819783663

Pre-trained language model-based methods for Chinese Grammatical Error Correction (CGEC) are categorized into Seq2Seq and Seq2Edit types. However, both Seq2Seq and Seq2Edit models depend on high-quality training data significantly. Considering the strong generation and inference ability of large language models (LLMs), we propose a large language model-guided optimization training method to exploit LLMs to extract error knowledge to optimize the traditional CGEC model training process. On the one hand, we use error types and confusion sets as extra knowledge to guide LLMs to generate diverse pseudo data, thus extending the error distribution of our training data. On the other hand, LLMs are utilized to infer the predicted results from our CGEC models and obtain the re-training data, thus iteratively optimizing our pre-trained CGEC models. Experiments on two benchmark datasets show that our LLMs-guided optimization method with small-scale training data can achieve comparable results with baseline models with large-scale training data. Detailed comparison experiments demonstrate that both the early deviser pseudo data and the later re-training data are extremely useful for traditional CGEC model optimization training, and can benefit from each other. We will release our code and prompts at https://***/SakuraAcedia/llm-cgec-got to facilitate future work. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Error correction

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A Remaining Useful Life Prediction of Turbofan Engines Based on Multi-Scale Temporal Convolutional Networks with Dual Squeeze-Excitation Attention Mechanism

A Remaining Useful Life Prediction of Turbofan Engines Based...

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2024 International Conference on Computational Intelligence and Communication System, CCICS 2024

作者： Wang, Hairui Li, Dongjun Li, Dongwen Li, Ya Zhu, Guifu Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan Kunming650504 China Information Technology Construction Management Center Kunming University of Science and Technology Yunnan Kunming650504 China

To better handle temporal data and delve into learning the features of the data, a turbofan engine residual life prediction method is proposed, which integrates a dual-squeeze-excitation attention mechanism with a multi-scale temporal convolutional network. Firstly, utilizing a sliding window, the extracted multi-dimensional sensor features undergo overlapping sampling to enhance the model's perception of temporal data. Secondly, a hybrid network prediction model based on DSE-MTCN is constructed, employing multi-scale convolutional kernels to expand the receptive field of convolution, assigning different weights to features, and adaptively allocating weights to hidden layer units. Lastly, the DSE-MTCN prediction model is globally optimized using the RAdam algorithm. The results demonstrate that this method effectively enhances the accuracy and generalization ability of the prediction model. © Published under licence by IOP Publishing Ltd.

关键词： Prediction models

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Convolutional Neural Networks Aided Reinforcement Learning for Accelerated Optimization of Antenna Topology

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Applied Computational Electromagnetics Society Journal 2025年第1期40卷 35-41页

作者： Dou, Jiangling Gong, Hao Wei, Shuaibing Chen, Haokang Chen, Yujie Shen, Tao Song, Jian Yunnan Key Laboratory of Computer Technologies Application Kunming University of Science and Technology Kunming650500 China School of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China College of Mechanical and Electrical Engineering Yunnan Electromechanical Vocational and Technical College Kunming650500 China

A machine learning (ML) framework is proposed to achieve the automatic and rapid optimization of antenna topologies. A convolutional neural network (CNN) is utilized as a surrogate model (SM) and is combined with reinforcement learning (RL) algorithms. Specifically, the RL agent interacts with simulation software to learn. Data accumulated from electromagnetic (EM) simulations are used to train the SM. The CNN-based SM predicts antenna performance based on the topology of the antenna. Subsequently, the SM replaces EM simulations within the RL training environment. The RL agent interacts with the CNN-based SM to search for the optimal topology. This approach significantly reduces dependence on time-consuming EM simulations. To validate the effectiveness of the optimization method, a center-fed microstrip patch antenna is optimized. Simulation results demonstrate that, compared to other optimization methods, impedance bandwidth is improved, while the number of simulation samples and optimization time are significantly reduced. © ACES.

关键词： Convolutional neural networks

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USDE-Based Task-Space Bilateral Teleoperator Control Design with Time Delay 16th

USDE-Based Task-Space Bilateral Teleoperator Control Design ...

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16th International Conference on Modelling, Identification and Control, ICMIC 2024

作者： Luo, Xin Ma, Jun Wang, Xian Na, Jing Xing, Yashan Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Faculty of Mechanical and Electrical Engineering Kunming University of Science and Technology Kunming650500 China Yunnan Key Laboratory of Intelligent Control and Application Kunming University of Science and Technology Kunming650500 China

ISBN: (纸本)9789819617760

In this paper, we present a control strategy for bilateral teleoperators operated in the task space, which is designed to estimate the uncertain dynamics. To implement this proposed strategy, we develop a straightforward yet robust unknown system dynamics estimator (USDE) by incorporating a low-pass filtering operation. By using the USDE, we can eliminate the impact of unmodeled system dynamics without the need to measure the acceleration signal. Furthermore, the proposed USDE offers the advantages of requiring only one tuning parameter and being easily adjustable compared to conventional estimators. The convergence of the estimation error associated with the unmodeled system dynamics and the stability of the closed-loop control system are both rigorously demonstrated through Lyapunov’s theorem. Ultimately, the superior performance of our method, as evidenced by enhancing motion synchronization and stability, is confirmed through simulation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Time delay

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