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Identifying and Predicting Voluntary Peeling Force in Continuous Curvilinear Capsulorhexis Considering Species Differences

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Zheng, Yu Yang, Yang Ma, Ke Lin, Chuang Pei, Yajing Lu, Mingfeng Ji, Yi Nanjing Univ Posts & Telecommun Coll Automat Nanjing 210023 Peoples R China Nanjing Univ Posts & Telecommun Coll Artificial Intelligence Nanjing 210023 Peoples R China Beihang Univ Sch Mech Engn & Automat Beijing 100191 Peoples R China Capital Med Univ Beijing Tongren Hosp Beijing Inst Ophthalmol Beijing Tongren Eye CtrBeijing Ophthalmol & Visua Beijing 100730 Peoples R China Beijing Xianwei Med Technol Co Ltd Beijing 100083 Peoples R China Shanxi Med Univ Dept Ophthalmol Shanxi Eye Hosp Taiyuan 030002 Shanxi Peoples R China Sun Yat Sen Univ Zhongshan Ophthalm Ctr State Key Lab Ophthalmol Guangdong Prov Key Lab Ophthalmol & Visual Sci Guangzhou 510060 Peoples R China Beijing Informat Sci & Technol Univ Sch Automat Beijing 100192 Peoples R China

A handheld manipulator offers a feasible way to relieve the challenge of continuous curvilinear capsulorhexis (CCC) by controlling peeling force actively. The precise identification and prediction of voluntary peeling force (0-2 Hz) form the basis of force controller design. However, achieving this goal is limited by species differences (experimental validation: animal model and clinical surgery: in vivo human). This article proposes a framework to relieve this problem. The proposed framework consists of a wavelet-based process module and a prediction module based on long short-term memory (LSTM) and the transformer's encoder. Peeling forces measured from ex-vivo pig eyes (no treatment, treated with 0.1% and 0.5% sodium hypochlorite), in vivo rabbit eyes, and ex-vivo human lenses are utilized to validate the proposed framework. Experimental results show that the root-mean-square (rms) error of identification is 0.37 mN. The rms error of prediction is 0.41 mN (steady) and 0.87 mN (sharp, prediction time: 50 ms), respectively. The rms prediction error of ex-vivo human samples is 0.36 mN (steady) and 1.05 mN (sharp), even though all training data come from animal models.

关键词： Force Long short term memory transformers Lenses Force measurement Rabbits Ophthalmology Surgery Root mean square Logic gates Continuous curvilinear capsulorhexis (CCC) long short-term memory (LSTM) transformer encoder voluntary peeling force wavelet decomposition

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Improved object detection method for autonomous driving based on DETR

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FRONTIERS IN NEUROROBOTICS 2025年 18卷 1484276页

作者： Zhao, Huaqi Zhang, Songnan Peng, Xiang Lu, Zhengguang Li, Guojing Jiamusi Univ Sch Informat & Elect Technol Heilongjiang Prov Key Lab Autonomous Intelligence Jiamusi Peoples R China Jiamusi Univ Sch Mat Sci & Engn Jiamusi Peoples R China

Object detection is a critical component in the development of autonomous driving technology and has demonstrated significant growth potential. To address the limitations of current techniques, this paper presents an improved object detection method for autonomous driving based on a detection transformer (DETR). First, we introduce a multi-scale feature and location information extraction method, which solves the inadequacy of the model for multi-scale object localization and detection. In addition, we developed a transformer encoder based on the group axial attention mechanism. This allows for efficient attention range control in the horizontal and vertical directions while reducing computation, ultimately enhancing the inference speed. Furthermore, we propose a novel dynamic hyperparameter tuning training method based on Pareto efficiency, which coordinates the training state of the loss functions through dynamic weights, overcoming issues associated with manually setting fixed weights and enhancing model convergence speed and accuracy. Experimental results demonstrate that the proposed method surpasses others, with improvements of 3.3%, 4.5%, and 3% in average precision on the COCO, PASCAL VOC, and KITTI datasets, respectively, and an 84% increase in FPS.

关键词： object detection feature extraction transformer encoder loss function parameter tuning

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Fusing Micro- and Macro-Scale Information to Predict Anticancer Synergistic Drug Combinations

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2025年第3期29卷 2297-2309页

作者： Wang, Xiaowen Zhu, Hongming Liu, Qi Liu, Qin Tongji Univ Sch Software Engn Shanghai 201804 Peoples R China Tongji Univ Tongji HospFrontier Sci Ctr Stem Cell Res Frontier Sci Ctr Stem Cell ResMinist EducBioinfo Sch MedKey Lab Spine & Spinal Cord Injury Repair Shanghai 200092 Peoples R China

Drug combination therapy is highly regarded in cancer treatment. Computational methods offer a time- and cost-effective opportunity to explore the vast combination space. Although deep learning-based prediction methods lead the field, their generalization ability remains unsatisfactory. Few previous studies have the ability to finely characterize drugs and cell lines at both the micro-scale and macro-scale. Furthermore, the interaction of cross-scale information is often overlooked. These two points limit models' ability of predicting the synergism of drug combinations in cell lines. To address the issues, we propose a novel anticancer synergistic drug combination prediction method termed MMFSynergy in this article. The construction of MMFSynergy involves three phases. First, MMFSynergy pretrains two micro encoders and a macro graph encoder, which can capture micro- or macro-scale information from large volumes of unlabeled data and generate generic features for drugs and proteins. Second, it represents drugs and proteins by fusing cross-scale information through a self-supervised task. Finally, it employs a transformer encoder-based model to predict synergy scores, taking representations of drugs in the combinations and the associated proteins of cell lines as input. We compared our method with eight advanced methods across three typical scenarios based on two public datasets. The results consistently demonstrated that the proposed method's generalization ability outperforms six advanced methods'. We also conducted experiments including but not limited to ablation study and case study to further exhibit the effectiveness of MMFSynergy.

关键词： Drugs Predictive models Proteins Data models Computational modeling Biological system modeling Diffusion tensor imaging Computer architecture transformers Microprocessors Combination therapy drug combination prediction pretraining multi-scale feature fusion transformer encoder

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EnsembleSleepNet: a novel ensemble deep learning model based on transformers and attention mechanisms using multimodal data for sleep stages classification

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APPLIED INTELLIGENCE 2025年第7期55卷 1-21页

作者： Mostafaei, Sahar Hassanzadeh Tanha, Jafar Sharafkhaneh, Amir Univ Tabriz Fac Elect & Comp Engn POB 51666-16471 Tabriz Iran Baylor Coll Med Sect Pulm Crit Care & Sleep Med Dept Med Med Houston TX USA

Classifying sleep stages using biological signals is an important and challenging task in sleep medicine. Combining deep learning networks with transformers and attention mechanisms represents a powerful approach for achieving high-performance results in classification tasks. Multimodal learning, which integrates various types of input data, can significantly enhance the classification performance of these networks. However, many existing studies either rely on single-modal data or design a single model to handle different signals and modalities without considering the unique characteristics of each data type, which often fails to capture optimal features. To address this limitation, we propose an ensemble model for sleep stage classification that leverages multimodal data, including raw signals, spectrograms, and handcrafted features. We utilize the Sleep Heart Health Study (SHHS) dataset by selecting multiple signals from polysomnography recordings. Our approach develops three specialized sub-models with different layers and components, each designed based on the unique characteristics of specific data types and signals, and integrates them into a unified ensemble deep learning framework. The proposed EnsembleSleepNet achieved comparable performance against existing methods by obtaining high values of 0.897, 0.852, and 0.831 in accuracy, Cohen's kappa (kappa), and Macro F1 score (MF1) respectively. Additionally, ablation studies revealed the impact of the selected signals and components in our developed model.

关键词： Deep Learning Attention mechanism Ensemble models transformer encoder Multimodal data Sleep Staging Polysomnography

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Improving multi-step dissolved oxygen prediction in aquaculture using adaptive temporal convolution and optimized transforme

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2025年 235卷

作者： Shao, Kaixuan Li, Daoliang Tang, Hao Zhang, Yonghui Xu, Bo Bhatti, Uzair Aslam Hainan Univ Sch Informat & Commun Engn 58 Renmin Ave Haikou 570228 Hainan Peoples R China China Agr Univ Natl Innovat Ctr Digital Fishery 17 Qinghua East Rd Beijing 100083 Peoples R China China Agr Univ Coll Informat & Elect Engn 17 Qinghua East Rd Beijing 100083 Peoples R China

Accurate dissolved oxygen (DO) prediction is crucial for optimizing aquaculture efficiency. However, existing forecasting methods often struggle to capture periodic patterns, model complex feature dependencies, and maintain high accuracy in multi-step predictions. To address these challenges, this study proposes an enhanced transformer-based model designed to improve both prediction accuracy and stability. The model first integrates an Adaptive Temporal Convolutional Network (ATCN) to extract periodic patterns and local temporal features from time-series data. Then, a transformer encoder with linear attention mechanisms and relative position encoding is employed to enhance feature extraction and sequence modeling. To further strengthen temporal dependency learning, the conventional decoder is replaced with a modified Gated Recurrent Unit (GRU), and a linear regression-based error correction mechanism is introduced to refine multi-step forecasting accuracy. Experimental results on Public Dataset 3 demonstrate that the proposed model achieves an average MAE of 1.624 and RMSE of 2.254, reflecting improvements of 45.06% and 40.57%, respectively, compared to the baseline transformer model. These findings highlight the model's ability to effectively capture complex temporal dependencies, significantly enhancing DO prediction accuracy and robustness in multi-step forecasting tasks. Additionally, the model demonstrates strong performance in pH prediction, underscoring its potential for multiparameter water quality forecasting. This work provides valuable insights for improving water quality management and mitigating risks in aquaculture.

关键词： Dissolved oxygen prediction Aquaculture transformer encoder Adaptive Temporal Convolutional Network Time series prediction

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A novel twin vision transformer framework for crop disease classification with deformable attention

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2025年 105卷

作者： Padshetty, Smitha Ambika Poojya Doddappa Appa Coll Engn Dept Comp Sci & Engn Kalaburagi Karnataka India Sharnbasva Univ Kalaburagi Dept Comp Sci & Engn Kalaburagi Karnataka India

In recent years, crop disease has grown substantially due to the lack of immunity in crops and climate changes. This results in crop destruction and poor crop cultivation. Quick and accurate classification of crop disease is crucial for enhancing agricultural yield and preventing crop damage. This research proposes a novel Twin Vision transformer framework for accurate and quick classification of crop disease. The proposed model utilized a Depthwise Separable Visual Geometry Group (16) technique to extract the crop image features that incorporated Depthwise Separable Convolution and channel normalization. The Depthwise Separable Convolution is deployed for computing the channel and space separation and mitigating the computational complexity. The channel normalization is employed to normalize each activation using local responses. Further, the Twin Vision transformer model integrates a transformer encoder, Edge Aware Enhancement Module, and classifier head for accurate disease classification. The transformer encoder layer is applied to capture the complex relationship that encompasses deformable attention for identifying the image patch's relevant part and enhancing the flexibility of the model, and spatial-channel attention for enhancing the crucial feature channels and spatial locations. Moreover, the residual Multilayer Perceptron with Swish-Gated Linear Units is employed to enhance the generalization capability of the model by mitigating overfitting. The Edge Aware Enhancement Module is developed to improve the features around the identified edges. Additionally, the Squeeze and Excitation block in the classifier head is applied to emphasize the features of crop disease and to enhance the model's classification accuracy. The proposed Twin Vision transformer model provided a higher classification accuracy of 98.88 %, and the experimental analysis confirms that the proposed Twin Vision transformer model is more accurate and reliable in classifying crop disease than

关键词： Crop disease transformer encoder Swish-Gated Linear Units Deformable attention Depthwise Separable Convolution Edge Aware Enhancement Module Classification

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Mixed supervised cross-subject seizure detection with transformer and reference learning

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APPLIED SOFT COMPUTING 2025年 175卷

作者： He, Landi Ji, Dezan Dong, Xingchen Li, Haotian Liu, Guoyang Zhou, Weidong Shandong Univ Sch Integrated Circuits Jinan 250100 Peoples R China Shandong Univ Shenzhen Inst Shenzhen 518057 Peoples R China

Automatic seizure detection aims to identify occurrences of epileptic seizures, enabling timely seizure intervention and protecting patients' safety. In recent years, deep learning has significantly promoted the research progress in the field of seizure detection. In this paper, we propose a cross-subject seizure detection system based on a transformer encoder. A novel data fusion approach is leveraged to mitigate the imbalance issue of seizure and non-seizure data. Meanwhile, a new mapping method is employed to replace traditional feature extractors, effectively enhancing the real-time capabilities of the system. A mixed supervised and unsupervised learning approach, coupled with a specially designed loss function, is utilized to strengthen the model's ability to capture temporal and spatial features in electroencephalogram (EEG) signals. Furthermore, an innovative learning strategy named reference learning is proposed to enhance the model's generalization performance. Finally, the proposed system was evaluated on the publicly available CHB-MIT dataset using the Leave-One-Out CrossValidation (LOOCV) strategy. The system achieved a segment-based sensitivity of 91.06 % and an event-based sensitivity of 93.59 % in the cross-subject seizure detection task.

关键词： Electroencephalogram (EEG) Epilepsy Cross-subject seizure detection transformer encoder Mixed supervised learning

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PE-DOCC: A Novel Periodicity-Enhanced Deep One-Class Classification Framework for Electricity Theft Detection

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APPLIED SCIENCES-BASEL 2025年第4期15卷 2193-2193页

作者： Wu, Zhijie Wang, Yufeng Nanjing Univ Posts & Telecommun Sch Commun & Informat Engn Nanjing 210003 Peoples R China

Electricity theft, emerging as one of the severe cyberattacks in smart grids, causes significant economic losses. Due to the powerful expressive ability of deep neural networks (DNN), supervised and unsupervised DNN-based electricity theft detection (ETD) schemes have experienced widespread deployment. However, existing works have the following weak points: Supervised DNN-based schemes require abundant labeled anomalous samples for training, and even worse, cannot detect unseen theft patterns. To avoid the extensively labor-consuming activity of labeling anomalous samples, unsupervised DNNs-based schemes aim to learn the normality of time-series and infer an anomaly score for each data instance, but they fail to capture periodic features effectively. To address these challenges, this paper proposes a novel periodicity-enhanced deep one-class classification framework (PE-DOCC) based on a periodicity-enhanced transformer encoder, named Periodicformer encoder. Specifically, within the encoder, a novel criss-cross periodic attention is proposed to capture both horizontal and vertical periodic features. The Periodicformer encoder is pre-trained by reconstructing partially masked input sequences, and the learned latent representations are then fed into a one-class classification for anomaly detection. Extensive experiments on real-world datasets demonstrate that our proposed PE-DOCC framework outperforms state-of-the-art unsupervised ETD methods.

关键词： electricity theft detection one-class classification transformer encoder unsupervised representation learning

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A new dual-channel transformer-based network for remaining useful life prediction

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MEASUREMENT SCIENCE AND TECHNOLOGY 2025年第2期36卷 026119-026119页

作者： Yang, Kai Wei, Yuxuan Ma, Yubao Huang, Lehong Tang, Qiang Li, Zhiguo Chinese Acad Sci Xian Inst Opt & Precis Mech Xian Shaanxi Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci Key Lab Space Precis Measurement Technol Xian Shaanxi Peoples R China

Remaining useful life (RUL) prediction is crucial for enhancing the reliability of complex systems, preventing unexpected failures, and reducing the maintenance costs of electromechanical systems. Data-driven methods, particularly deep learning, have been extensively applied in RUL prediction. However, recent methods have focused solely on long-term features, neglecting the significant role of short-term features in long sequence processes, which leads to an inability to achieve high-precision RUL prediction. In this article, a novel dual-channel network is proposed, which is based on the transformer encoder. Additionally, the approach proposes a robust dual-channel feature extraction layer that integrates a multi-scale convolution block and a gating convolution block. This integrated system is capable of capturing both long-term and short-term features from the raw data simultaneously. Compared to convolutional neural networks, recurrent neural networks and other transformer-based methods, the proposed method is more effective at extracting profound features and focusing on important information in the input. Utilizing the self-attention mechanism, the proposed model captures degradation features and achieves precise RUL prediction. Experiments conducted on the CMAPSS dataset demonstrate that the proposed method outperforms recent RUL prediction methods. The average root mean square error of the proposed method is reduced by 6.8%, and the average score is reduced by 8.5%, proving the effectiveness of the proposed approach.

关键词： remaining useful life transformer encoder gating mechanism turbofan engine

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A Multi-Granularity Features Representation and Dimensionality Reduction Network for Website Fingerprinting

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IEEE ACCESS 2025年 13卷 574-587页

作者： Ding, Yaojun Hu, Bingxuan Gansu Univ Polit Sci & Law Sch Artificial Intelligence Lanzhou 730070 Peoples R China Gansu Univ Polit Sci & Law Sch Cyberspace Secur Lanzhou 730070 Peoples R China

Website fingerprinting (WF) refers to the identification of target websites accessed by users in anonymous communication scenarios, playing a critical role in cybercrime investigation and forensics. In recent years, deep learning-based WF has become a research focus in this field. Although these methods can achieve high accuracy, their performance tends to decline over time. This degradation results from the evolution of network protocol versions and the ongoing development of obfuscation techniques, a phenomenon known as concept drift. To address the problem of concept drift, this paper presents a multi-granularity features representation and dimensionality reduction network for Website Fingerprinting, referred to as LRCT. The LRCT network effectively leverages the temporal learning advantages of Local Recurrent Networks (Local RNN) and the spatial learning strengths of Convolutional Neural Network (CNN) by designing the local feature extraction block (denoted as LRC Block), which extracts fine-grained local features from 2000-dimensional original sequences and reduces the dimensionality to 125. The network then uses a transformer encoder to capture more robust global features from the low-dimensional data. Experimental results comparing LRCT with state-of-the-art WF methods demonstrate that the proposed LRCT achieves 99.34% accuracy in a closed-world scenario, outperforming other benchmark models. Compared to the benchmark WF-transformer method, the LRCT network reduces the parameter count by half and decreases the average training time per epoch to approximately 20% of the benchmark. Even under concept drift conditions, LRCT maintains an accuracy of 89.9%. In open-world scenarios, LRCT also has better precision and recall.

关键词： Feature extraction Fingerprint recognition Accuracy Computer architecture Monitoring Convolutional neural networks Concept drift Microprocessors transformers Long short term memory Website fingerprinting local recurrent neural network transformer encoder traffic analysis

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