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学者姓名：李建强
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Abstract ：
Motor coordination is crucial for preschoolers' development and is a key factor in assessing childhood development. Current diagnostic methods often rely on subjective manual assessments. This paper presents a machine vision-based approach aimed at improving the objectivity and adaptability of assessments. The method proposed involves the extraction of key points from the human skeleton through the utilization of a lightweight pose estimation network, thereby transforming video assessments into evaluations of keypoint sequences. The study uses different methods to handle static and dynamic actions, including regularization and Dynamic Time Warping (DTW) for spatial alignment and temporal discrepancies. A penalty-adjusted single-frame pose similarity method is used to evaluate actions. The lightweight pose estimation model reduces parameters by 85%, uses only 6.6% of the original computational load, and has an average detection missing rate of less than 1%. The average error for static actions is 0.071 with a correlation coefficient of 0.766, and for dynamic actions it is 0.145 with a correlation coefficient of 0.653. These results confirm the proposed method's effectiveness, which includes customized visual components like motion waveform graphs to improve accuracy in pediatric healthcare diagnoses.
Keyword ：
Correlation coefficient Correlation coefficient Motor coordination Motor coordination decision support system decision support system Pose estimation Pose estimation Visualization Visualization dynamic time warping dynamic time warping Skeleton Skeleton Manuals Manuals developmental coordination disorder developmental coordination disorder Accuracy Accuracy Machine vision Machine vision action quality assessment action quality assessment Pediatrics Pediatrics Reliability Reliability pose estimation pose estimation
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| GB/T 7714 | Lei, Yi , Shu, Dawei , Yu, Miao et al. Evaluation Method of Motor Coordination Ability in Children Based on Machine Vision [J]. | TSINGHUA SCIENCE AND TECHNOLOGY , 2025 , 30 (2) : 633-649 . |
| MLA | Lei, Yi et al. "Evaluation Method of Motor Coordination Ability in Children Based on Machine Vision" . | TSINGHUA SCIENCE AND TECHNOLOGY 30 . 2 (2025) : 633-649 . |
| APA | Lei, Yi , Shu, Dawei , Yu, Miao , Shi, Donglin , Li, Jianqiang , Chen, Yanjie . Evaluation Method of Motor Coordination Ability in Children Based on Machine Vision . | TSINGHUA SCIENCE AND TECHNOLOGY , 2025 , 30 (2) , 633-649 . |
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Abstract ：
With the increasing amount of cloud-based speech files, the privacy protection of speech files faces significant challenges. Therefore, integrity authentication of speech files is crucial, and there are two pivotal problems: (1) how to achieve fine-grained and highly accurate tampering detection and (2) how to perform high-quality tampering recovery under high tampering ratios. Tampering detection methods and tampering recovery methods of existing speech integrity authentication are mutually balanced, and most tampering recovery methods are carried out under ideal tampering conditions. This paper proposes an encrypted speech integrity authentication method that can simultaneously address both of problems, and its main contributions are as follows: (1) A 2-least significant bit (2-LSB)-based dual fragile watermarking method is proposed to improve tampering detection performance. This method constructs correlations between encrypted speech sampling points by 2-LSB-based fragile watermarking embedding method and achieves low-error tampering detection of tampered sampling points based on four types of fragile watermarkings. (2) A speech self-recovery model based on residual recovery-based linear interpolation (R2-Lerp) is proposed to achieve tampering recovery under high tampering ratios. This method constructs the model based on the correlation between tampered sampling points and their surrounding sampling points and refines the scenarios of the model according to the tampering situation of the sampling points, with experimental results showing that the recovered speech exhibits improved auditory quality and intelligibility. (3) A scrambling encryption algorithm based on the Lorenz mapping is proposed as the speech encryption method. This method scrambles the speech sampling points several times through 4-dimensional chaotic sequence, with experimental results showing that this method not only ensures security but also slightly improves the effect of tampering recovery.
Keyword ：
tamper recovery tamper recovery least significant bit least significant bit tamper detection tamper detection residual recovery-based linear interpolation residual recovery-based linear interpolation encrypted speech integrity authentication encrypted speech integrity authentication
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| GB/T 7714 | Xu, Fujiu , Li, Jianqiang , Xu, Xi . An Encrypted Speech Integrity Authentication Method: Focus on Fine-Grained Tampering Detection and Tampering Recovery Under High Tamper Ratios [J]. | MATHEMATICS , 2025 , 13 (4) . |
| MLA | Xu, Fujiu et al. "An Encrypted Speech Integrity Authentication Method: Focus on Fine-Grained Tampering Detection and Tampering Recovery Under High Tamper Ratios" . | MATHEMATICS 13 . 4 (2025) . |
| APA | Xu, Fujiu , Li, Jianqiang , Xu, Xi . An Encrypted Speech Integrity Authentication Method: Focus on Fine-Grained Tampering Detection and Tampering Recovery Under High Tamper Ratios . | MATHEMATICS , 2025 , 13 (4) . |
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Abstract ：
Fundus images can be obtained non-invasively and be adopted to monitor the follow-up on various fundus diseases, such as high myopia. Therefore, the use of fundus images for the early screening of eye diseases has principal clinical significance. However, AI-based medical research continues to face two main challenges, lack of prior-knowledge guidance and complex fundus information. In this paper, we propose a pediatric high myopia classification model, the Attention-based Patch Residual Shrinkage network (APRSnet), which facilitates early clinical diagnosis by evaluating the importance of different image features. A collection of 2492 high-resolution fundus images of children, including 768 images of high myopia and 1724 images of non-high myopia, is fed to APRSnet as the training dataset. To better understand how different features impact the classification, APRSnet is tested on multiple feature-enhanced fusion datasets. The result shows that, by removing irrelevant information in fundus images, APRSnet achieves an accuracy of 0.959 and an F1 score of 0.946 and outperforms all classic image classification networks we compared with. We also testify that, in fundus images, gradient information benefits severity classification the most since it helps with model convergence more than luminance and texture information.
Keyword ：
Deep learning Deep learning Computer-aided diagnosis Computer-aided diagnosis Fundus images Fundus images Pediatric high myopia Pediatric high myopia
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| GB/T 7714 | Peng, Haoran , Li, Jianqiang , Cheng, Wenxiu et al. Automatic diagnosis of pediatric high myopia via Attention-based Patch Residual Shrinkage network [J]. | EXPERT SYSTEMS WITH APPLICATIONS , 2024 , 255 . |
| MLA | Peng, Haoran et al. "Automatic diagnosis of pediatric high myopia via Attention-based Patch Residual Shrinkage network" . | EXPERT SYSTEMS WITH APPLICATIONS 255 (2024) . |
| APA | Peng, Haoran , Li, Jianqiang , Cheng, Wenxiu , Zhao, Linna , Guan, Yu , Li, Zhaosheng et al. Automatic diagnosis of pediatric high myopia via Attention-based Patch Residual Shrinkage network . | EXPERT SYSTEMS WITH APPLICATIONS , 2024 , 255 . |
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Abstract ：
Disease diagnosis represents a critical and arduous endeavor within the medical field. Artificial intelligence (AI) techniques, spanning from machine learning and deep learning to large model paradigms, stand poised to significantly augment physicians in rendering more evidence-based decisions, thus presenting a pioneering solution for clinical practice. Traditionally, the amalgamation of diverse medical data modalities (e.g., image, text, speech, genetic data, physiological signals) is imperative to facilitate a comprehensive disease analysis, a topic of burgeoning interest among both researchers and clinicians in recent times. Hence, there exists a pressing need to synthesize the latest strides in multi-modal data and AI technologies in the realm of medical diagnosis. In this paper, we narrow our focus to five specific disorders (Alzheimer's disease, breast cancer, depression, heart disease, epilepsy), elucidating advanced endeavors in their diagnosis and treatment through the lens of artificial intelligence. Our survey not only delineates detailed diagnostic methodologies across varying modalities but also underscores commonly utilized public datasets, the intricacies of feature engineering, prevalent classification models, and envisaged challenges for future endeavors. In essence, our research endeavors to contribute to the advancement of diagnostic methodologies, furnishing invaluable insights for clinical decision making.
Keyword ：
disease diagnosis disease diagnosis artificial intelligence artificial intelligence multi-modal data multi-modal data machine learning machine learning deep learning deep learning large model large model
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| GB/T 7714 | Xu, Xi , Li, Jianqiang , Zhu, Zhichao et al. A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis [J]. | BIOENGINEERING-BASEL , 2024 , 11 (3) . |
| MLA | Xu, Xi et al. "A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis" . | BIOENGINEERING-BASEL 11 . 3 (2024) . |
| APA | Xu, Xi , Li, Jianqiang , Zhu, Zhichao , Zhao, Linna , Wang, Huina , Song, Changwei et al. A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis . | BIOENGINEERING-BASEL , 2024 , 11 (3) . |
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Abstract ：
Pollen image classification is crucial for understanding allergic reactions and environmental impacts. In this study, we propose RESwinT, an enhanced deep learning model specifically designed for pollen image classification. RESwinT incorporates a parallel window transformer block with contextual information aggregation to expand the receptive field and facilitate information exchange between image patches. Additionally, a coordinate attention module is integrated to emphasize channel-specific features, improving the model's focus on salient pollen characteristics. Experiments conducted on a locally developed dataset of eight allergenic pollen types from Beijing, China, demonstrate that RESwinT achieves state-of-the-art performance, with an F1-score of 0.985 and an accuracy of 98.58%, surpassing existing CNN and transformer-based methods. These results highlight the effectiveness of RESwinT in pollen image classification and its potential for wider applications in environmental monitoring and healthcare.
Keyword ：
Parallel window transformer block Parallel window transformer block Pollen images classification Pollen images classification Coordinate attention Coordinate attention Swin transformer Swin transformer
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| GB/T 7714 | Zu, Baokai , Cao, Tong , Li, Yafang et al. RESwinT: enhanced pollen image classification with parallel window transformer and coordinate attention [J]. | VISUAL COMPUTER , 2024 , 41 (7) : 4975-4990 . |
| MLA | Zu, Baokai et al. "RESwinT: enhanced pollen image classification with parallel window transformer and coordinate attention" . | VISUAL COMPUTER 41 . 7 (2024) : 4975-4990 . |
| APA | Zu, Baokai , Cao, Tong , Li, Yafang , Li, Jianqiang , Wang, Hongyuan , Wang, Quanzeng . RESwinT: enhanced pollen image classification with parallel window transformer and coordinate attention . | VISUAL COMPUTER , 2024 , 41 (7) , 4975-4990 . |
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Abstract ：
Diabetic retinopathy (DR) significantly burdens ophthalmic healthcare due to its wide prevalence and high diagnostic costs. Especially in remote areas with limited medical access, undetected DR cases are on the rise. Our study introduces an advanced deep transfer learning-based system for real-time DR detection using fundus cameras to address this. This research aims to develop an efficient and timely assistance system for DR patients, empowering them to manage their health better. The proposed system leverages fundus imaging to collect retinal images, which are then transmitted to the processing unit for effective disease severity detection and classification. Comprehensive reports guide subsequent medical actions based on the identified stage. The proposed system achieves real-time DR detection by utilizing deep transfer learning algorithms, specifically VGGNet. The system's performance is rigorously evaluated, comparing its classification accuracy to previous research outcomes. The experimental results demonstrate the robustness of the proposed system, achieving an impressive 97.6% classification accuracy during the detection phase, surpassing the performance of existing approaches. Implementing the automated system in remote areas has transformed healthcare dynamics, enabling early, cost-effective DR diagnosis for millions. The system also streamlines patient prioritization, facilitating timely interventions for early-stage DR cases.
Keyword ：
Morphology Morphology Retinal fundus Retinal fundus Retinopathy Retinopathy Micro-aneurysms Micro-aneurysms Hemorrhage Hemorrhage Diabetes Diabetes Health issues Health issues
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| GB/T 7714 | Jabbar, Ayesha , Naseem, Shahid , Li, Jianqiang et al. Deep Transfer Learning-Based Automated Diabetic Retinopathy Detection Using Retinal Fundus Images in Remote Areas [J]. | INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS , 2024 , 17 (1) . |
| MLA | Jabbar, Ayesha et al. "Deep Transfer Learning-Based Automated Diabetic Retinopathy Detection Using Retinal Fundus Images in Remote Areas" . | INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 17 . 1 (2024) . |
| APA | Jabbar, Ayesha , Naseem, Shahid , Li, Jianqiang , Mahmood, Tariq , Jabbar, Kashif , Rehman, Amjad et al. Deep Transfer Learning-Based Automated Diabetic Retinopathy Detection Using Retinal Fundus Images in Remote Areas . | INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS , 2024 , 17 (1) . |
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Abstract ：
一种基于PWC原则的深度学习光流估计方法涉及计算机处理技术领域。本发明利用全序列图像前后切片之间的变化特性，先生成光流图；给定两个输入图像，特征金字塔提取模块使用共享参数的卷积神经网络分别提取他们的特征表示金字塔。随后将生成的特征表示进行变形操作，接着直接计算两个特征所有像素之间的相关性。然后将特征的相关性结果、第一张图像的特征和上采样得到的粗糙光流拼接在一起作为输入，输出为当前层的光流。再将光流估计结果和光流估计网络中倒数第二层的特征输入修正网络后，上采样值原始尺寸即可得到最终光流。本发明获取高精度的估计效果，实现模型尺寸与计算精度之间的平衡。
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| GB/T 7714 | 李建强 , 谭卓斐 , 赵琳娜 . 一种基于PWC原则的深度学习光流估计方法 : CN202310145447.7[P]. | 2023-02-21 . |
| MLA | 李建强 et al. "一种基于PWC原则的深度学习光流估计方法" : CN202310145447.7. | 2023-02-21 . |
| APA | 李建强 , 谭卓斐 , 赵琳娜 . 一种基于PWC原则的深度学习光流估计方法 : CN202310145447.7. | 2023-02-21 . |
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Abstract ：
一种基于位置‑边界信息引导的图像对象分割方法属于计算机视觉领域。本发明通过结合两个模块的信息获得花粉分割图像：目标定位模块通过图像级标签训练的分类网络获得定位图，提供准确的位置信息；边界引导模块利用花粉轮廓先验知识匹配目标花粉获得显著图，得到细粒度的边界信息。本方法可以充分利用两者之间的互补关系，获得准确的目标花粉边界，并丢弃非目标对象(如杂质)像素，显著提高花粉图像分割质量。
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| GB/T 7714 | 李建强 , 高正凯 , 贾卓霖 et al. 一种基于位置-边界信息引导的图像对象分割方法 : CN202310304844.4[P]. | 2023-03-27 . |
| MLA | 李建强 et al. "一种基于位置-边界信息引导的图像对象分割方法" : CN202310304844.4. | 2023-03-27 . |
| APA | 李建强 , 高正凯 , 贾卓霖 , 王一霖 , 李欣阳 , 马天宝 et al. 一种基于位置-边界信息引导的图像对象分割方法 : CN202310304844.4. | 2023-03-27 . |
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Abstract ：
本发明公开了基于PSO‑GA‑LSTM模型的空气质量预测方法，首先将序列数据进行预处理，然后利用粒子群算法优化LSTM模型超参数，从而确定LSTM模型的网络结构；利用遗传算法优化LSTM模型初始的权值阈值，确定LSTM模型的权值阈值。最后将利用最佳超参数和最佳权值阈值，建立LSTM模型，对空气质量时间序列数据进行训练并预测。本发明克服了传统的预测方法预测过程中精度不高的问题，且利用粒子群和遗传算法对LSTM参数进行优化，避免模型陷入局部最优解的问题，提高了预测收敛速度。最终实现了对空气质量时间序列的预测，更精确预测空气质量变化的趋势。
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| GB/T 7714 | 王强 , 刘博 , 朱念 et al. 基于PSO-GA-LSTM模型的空气质量预测方法 : CN202310317140.0[P]. | 2023-03-25 . |
| MLA | 王强 et al. "基于PSO-GA-LSTM模型的空气质量预测方法" : CN202310317140.0. | 2023-03-25 . |
| APA | 王强 , 刘博 , 朱念 , 李建强 , 丁磊 . 基于PSO-GA-LSTM模型的空气质量预测方法 : CN202310317140.0. | 2023-03-25 . |
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Abstract ：
一种基于噪声指数指导的多尺度半监督目标分割方法，属于计算机视觉领域，针对现有的单一传统分割方法鲁棒性不强、特征捕获能力有限，以及深度学习方法U‑Net对上下文信息关注不足、全监督方法像素级标注复杂的问题。本发明涉及两个层面的信息融合：由于单一的传统分割方法特征捕获能力有限，因此可以融合多种传统方法得到更加准确的伪标签；由于深度学习网络U‑Net对上下文信息关注不足，可以在每个阶段的跳跃连接融合不同尺度的下采样特征图，这样融合的特征图将会携带多尺度背景信息，并保存细粒度目标位置信息，进而使网络关注更重要区域。
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| GB/T 7714 | 李建强 , 朱楚杰 , 赵琳娜 et al. 一种基于噪声指数指导的多尺度半监督目标分割方法 : CN202310821345.2[P]. | 2023-07-06 . |
| MLA | 李建强 et al. "一种基于噪声指数指导的多尺度半监督目标分割方法" : CN202310821345.2. | 2023-07-06 . |
| APA | 李建强 , 朱楚杰 , 赵琳娜 , 刘小玲 , 刘朝磊 , 宋霖涛 et al. 一种基于噪声指数指导的多尺度半监督目标分割方法 : CN202310821345.2. | 2023-07-06 . |
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