業績

Vision Transformers (ViTs) achieve strong performance in visual recognition but incur quadratic computational cost with respect to the number of tokens, motivating extensive research on token pruning and reduction. Most existing pruning methods estimate token importance directly from attention weights, implicitly assuming that attention magnitude provides a reliable proxy for semantic relevance across all layers. Our analysis shows that the validity of this assumption varies substantially with transformer depth and model scale, and can also depend on the training paradigm. Through a systematic analysis of attention selectivity using multiple concentration and stability measures, attention distributions in shallow layers tend to be highly diffuse and weakly discriminative, making attention-based scoring unreliable for early-stage pruning. In contrast, attention becomes increasingly informative in deeper layers as token representations mature. Motivated by this observation, a broad range of non-attention importance scores derived from token embeddings, including statistics- and similarity-based criteria, is examined. Across ViT variants and diverse training settings, these non-attention scores exhibit more stable pruning behavior in shallow layers, whereas attention-based scoring becomes effective only after sufficient representational discrimination is achieved. Importantly, the depth at which this transition occurs is model-dependent and not strictly monotonic, indicating that uniform attention-based pruning is fundamentally mismatched to the representational dynamics of ViTs. Based on these findings, token pruning is formulated as a layer-wise selection problem governed by the reliability of attention, and lightweight static routing configurations are investigated without retraining or dynamic inference control. For equivalent FLOPs, the resulting pruning patterns achieve a trade-off between accuracy and efficiency that is comparable to or superior to that of representative token reduction methods. Overall, these results establish token importance estimation in ViTs as an inherently layer-dependent problem shaped by representation maturity, model characteristics, and training paradigm rather than uniform attention magnitude.

Nano Unmanned Aerial Vehicle (UAV) platforms are well-suited for tasks in confined spaces, such as indoor single-person tracking. However, they are constrained by payload, and milliwatt (mW) level computing budgets. To address these limitations, we present a fully on-board tracking system featuring Feather You Only Look Once (FeatherYOLO), an ultra-lightweight detector tailored to the embedded processor, and a custom image-based visual servoing controller deployed on a 29 gram Crazyflie 2.1 nano UAV. FeatherYOLO utilizes a depthwise-separable backbone with a decoupled, anchor-free head, requiring 20 thousand parameters, 1.94 million multiply-accumulate operations, and 224 kilobytes of memory. On our self-collected indoor human detection dataset (six participants across five sites) under a cross-subject and cross-environment held-out protocol, the model achieved 99.5% mean Average Precision (mAP) at an Intersection Over Union threshold of 0.50 and 75.0% mAP over thresholds from 0.50 to 0.95. On-board profiling reveals that pure inference consumes 46.5 mW at 150 Frames Per Second (FPS), accounting for less than 1% of the total flight power, and outperforms a recent nano UAV baseline consuming 225.7 mW at 43 FPS. The proposed visual servoing strategy was refined through flight trials and task-driven tuning, transforming detector outputs into stable, bounded velocity commands with hysteresis and filtering for closed-loop indoor tracking. Real-world flight tests validated the tracking performance with an average tracking success rate of 90.0%, succeeding in 36 out of 40 experimental runs. The primary flight challenges identified include collisions and target loss.

Anaerobic digesters exhibit nonlinear dynamics, long input–output delays, irregular sampling, and operational constraints that complicate biogas prediction and control. This study develops a delay-aware digital-twin MPC benchmarking framework in which Anaerobic Digestion Model No. 1 (ADM1) serves as a mechanistic reference plant, while established machine-learning surrogates (Random Forest, KNN, SVR, XGBoost, LSTM, and TabPFN) provide fast one-step predictions under irregular measurements. A unified workflow integrates time-stamp alignment, sliding-window reconstruction, and Bayesian hyperparameter optimization. The surrogates are evaluated on an industrial dataset and an ADM1-based simulator incorporating a 7-day actuator delay, seasonal variability, noise, and missing data. The trained models are embedded in a constrained MPC layer, where multi-day inputs are optimized using Bayesian Optimization or Particle Swarm Optimization under hard bounds and daily ramp-rate limits. Both open-loop replay and closed-loop digital-twin MPC are investigated. Results show that PSO–MPC with inexpensive surrogates achieves the largest methane gains (up to approximately 25%), whereas BO–MPC is preferable for computationally expensive surrogates due to superior sample efficiency. Closed-loop simulations demonstrate that steady-state performance is preserved through feedback correction despite surrogate mismatch. The primary contribution is a reproducible digital-twin MPC scaffold enabling systematic integration and benchmarking of surrogate–optimizer combinations. The framework provides a reusable evaluation testbed for data-driven control of slow, delay-dominated biochemical processes, with potential extension to other chemical and energy systems subject to long delays and irregular monitoring.

In industrial surface defect detection, enhancing the model’s capability to express features at both coarse-grained and fine-grained levels is crucial. Accordingly, this paper proposes YOLO Coarse and Fine (YOLO-CF), a novel object detection model that significantly enhances feature expression by integrating innovative feature fusion strategies with an improved network architecture. YOLO-CF incorporates Res2Net and Res2Net block modules, notably improving the expression of fine-grained features without increasing computational complexity. Additionally, this model introduces a multi-scale feature fusion module, which seamlessly integrates coarse and fine-grained information by combining top-down and bottom-up pathways. This enhancement effectively expands the perceptual range and significantly improves the model’s generalization capability, making YOLO-CF a powerful tool for detecting diverse defects in complex industrial images. A hybrid downsampling module is introduced, combining max pooling, average pooling, and convolution operations with a stride of 2 to provide richer feature representations. In the GC10-DET dataset, YOLO-CF achieved a mean average precision (mAP) of 59.09%, surpassing the second-ranked RetinaNet by 3.41 percentage points. On the PCB, crack, NEU-DET and Dish-20 public datasets, YOLO-CF achieved mAPs of 97.38%, 84.72%, 73.35%, and 99.54%, at IoU =0.5, respectively. The experimental results indicate that by integrating feature extraction at both coarse-grained and fine-grained levels, YOLO-CF effectively enhances the model’s ability to detect objects of various sizes in complex scenes, demonstrating significant performance improvements. The code is available at http://www.ihpc.se.ritsumei.ac.jp/obidataset.html.

Reliable surface defect detection of annular industrial parts remains challenging in practical manufacturing environments due to curved geometries, reflective metallic surfaces, and severe data redundancy caused by continuous rotational scanning. Since adjacent frames contain highly overlapping visual information, conventional random splitting may cause rotational data leakage and overestimated performance. To address these issues, we propose an engineering-oriented data-to-deployment framework that applies lightweight deep learning to automated surface defect inspection. The framework integrates physics-guided data engineering, leak-aware evaluation, geometry-constrained annotation, lightweight detector design, and practical system deployment. First, a physics-based Sector Split strategy reduces the risk of rotational data leakage and aligns evaluation with the physical acquisition process. A geometry-constrained annotation tool further improves labeling consistency across consecutive rotational frames. Second, a Spatial-background suppression, Group-shuffle fusion, and Strip-attention enhanced You Only Look Once detector (YOLO-SGS) is developed for defect localization on reflective metallic surfaces. YOLO-SGS achieves 84.6% mean average precision at 50% intersection over union (mAP@50) with a model-only inference latency of 6.73 ms and 23.3% fewer parameters than the baseline detector. Five-fold physical-part-level cross-validation yields
81.2
±
1.9
%
mAP@50. On three unseen physical parts, the Sector Split model achieves 81.9% mAP@50, compared with 69.0% for the Random Split model. Finally, the AnnularInspector software system is implemented using an asynchronous multi-threaded architecture and achieves a mean end-to-end latency of 494.3 ms with no observed frame loss. External validation and industrial-interference tests further support cross-part generalization within the investigated setting and practical applicability under representative industrial disturbances.

Voxelizing active composite structures and controlling voxel-level material properties via 4D printing significantly expand design possibilities. However, as the number of voxels increases, the design space grows exponentially, posing significant challenges for predicting structural deformation. Here, a scalable deformation prediction method based on data mining is proposed. This method constructs a feature database using manually extracted features and employs the proposed curvature-driven sequence point generation (CSPG) algorithm to predict deformations for voxel encodings of arbitrary length.

Compared with the traditional finite element (FE) method, this approach significantly improves prediction efficiency, completing a single task within a second. In contrast to deep learning (DL) methods, this method improves prediction accuracy and effectively addresses the limited generalization ability of DL models when applied to structures beyond the training domain. To enhance usability, an interactive web-based platform is developed that allows users to customize voxel encodings and obtain end-to-end predictions. In addition to serving as an efficient tool for deformation prediction of active composite structures, this work introduces a novel pathway for the optimal design of complex intelligent structures.

Home-based lower limb rehabilitation robots (HLLRRs) serve as a valuable complement to clinical therapy by enabling rehabilitation in a home setting. However, ensuring these robots provide safe, personalized, and functional level-appropriate training remains a significant challenge. To address this issue, this study proposes a personalized gait generation method to deliver tailored trajectories and introduces a two-loop active compliance control strategy to integrate patients’ motion intentions into the robot’s control for robot-assisted training. Specifically, individual differences in physical body parameters are mapped into gait pattern variations using a convolutional neural network (CNN)- based gait generation model trained with RepOptimizer. The outer-loop trajectory planner incorporates the wearer’s motion intention into the change of step time according to the interaction torque and predicted system states. The inner-loop position controller is designed based on the active disturbance rejection control (ADRC) strategy. Simulation results demonstrate that the generated trajectories closely align with normal gait patterns, with loss within 0.13017, making them suitable as reference trajectories for robot-assisted walking training. The replanned trajectories remain smooth and preserve the characteristics of the original ones under various interaction torques. The designed controller tracks the desired trajectory quickly and steadily, with a mean absolute error (MAE) of less than 0.03107 rad, ensuring reliable performance throughout the training.

Industrial visual anomaly detection is critical for ensuring system reliability, safety, and efficiency. This paper presents a comprehensive survey of state-of-the-art anomaly detection techniques, analyzing methodologies, implementations, and recent advancements. Our survey aims to accelerate researchers’ understanding of emerging trends while providing a structured foundation for newcomers. We systematically review 196 recent papers covering five learning strategies, including fully supervised, semi-supervised, self-supervised, weakly supervised, and unsupervised approaches. This paper provides a detailed introduction to twelve industrial anomaly detection methods, revealing their theoretical foundations, technical principles, and practical applications. Additionally, we provide a detailed overview to 2D and 3D datasets for industrial visual anomaly detection. In addition, we critically analyze and summarize the experimental results, identify key performance indicators, and discuss the latest trends in the field of industrial anomaly detection. Beyond analysis, we contribute actionable insights for selecting optimal models for real-world deployment. Finally, we highlight open challenges and outline future research directions to drive innovation in this evolving field. The detailed resources are available at https://github.com/IHPCRits/IAD-Survey.

This paper presents a practical industrial visual anomaly detection framework that can detect and locate anomalous regions accurately. Specifically, we design an interaction-aware global attention framework (IGAF) comprising a pre-trained convolutional neural network as well as spatial and channel interaction-aware global attention module. The pre-trained convolutional neural network is employed for multi-scale feature extraction. The spatial interaction-aware global attention module helps the model focus on specific information by strengthening the correlation between certain channels. Meanwhile, the channel interaction-aware global attention module effectively captures critical regions in the input image and promotes an understanding of global spatial relationships. We evaluate IGAF on standard industrial visual anomaly detection datasets. The proposed IGAF achieves an outstanding result of 99.13% pixel-level AUROC with WideResNet50 as the backbone of the popular used MVTec AD dataset. IGAF achieves an impressive result of 97.51% pixel-level AUROC, even using a smaller backbone network such as ResNet18. Furthermore, IGAF achieves a remarkable result of 98.75% on the widely used VisA dataset. Additionally, IGAF demonstrates an excellent result of 92.40% on the real-world industrial anomaly detection dataset BTAD. These results indicate the effectiveness of IGAF for the detection and location of industrial vision anomalies.

Wireless capsule endoscopy is a non-invasive and painless approach for diagnosing gastrointestinal diseases. An automated medical decision support system can significantly enhance clinician efficiency and reduce the incidence of misdiagnosis when analyzing lesions within the 50,000 to 100,000 image frames generated for each individual. However, only a small number of previous studies have focused on fine-grained recognition characteristics, and very few have simultaneously addressed both fine-grained recognition and class imbalance issues in wireless capsule endoscopy. To address these issues, we propose a novel medical decision support network that includes a prototype attention enhancement module and a dynamic curriculum learning approach. The prototype attention enhancement module improves lesion-sensitive compact representation learning by leveraging cosine similarity between the representation and a prototype memory with multi-class centers. The dynamic curriculum learning adopts triplet loss and weighted cross-entropy, facilitated by a progressive factor that controls between fine-grained representation learning and class-sensitive learning. Through extensive comparative experiments on three public datasets, the proposed network demonstrated competitive performance, outperforming previous methods with an F1-score of 96.6% on the 10-class Kvasir-Capsule dataset, an accuracy of 98.3% on the CAD-CAP dataset, and an accuracy of 93.7% on the mixed KID dataset. Two additional gastrointestinal histopathology datasets confirm the generalization of the proposed network. The code will be available at https://github.com/Xingcun-Li/MDSN-WCE Note to Practitioners—The primary aim of this study is to address diagnostic challenges in automated wireless capsule endoscopy (WCE). WCE provides a non-invasive diagnostic method for gastrointestinal diseases, but manual analysis of the resulting large volume of image data is time-consuming and error-prone. To this end, we propose a novel medical decision support network, which is also applicable to similar medical image analysis issues characterized by class imbalance and fine-grained recognition. Our approach integrates a prototype attention enhancement (PAE) module and dynamic curriculum learning (DCL), enabling the model to accurately identify lesions within WCE images and exhibit favorable performance in gastrointestinal histopathology image recognition beyond the WCE domain. Additionally, our approach can be applied to various common deep learning models without incurring significant additional computational costs, thereby aiding endoscopists in significantly reducing the time required for diagnosing WCE video frames. The code will be uploaded to GitHub for direct use in the automated diagnosis of WCE video frames. While our method shows promise, its main focus is on rapidly identifying a large number of frames within WCE videos. However, further work is needed to extract key frames and improve the interpretability of outputs from WCE videos, which is also a future research direction.

Designing voxelized composite structures via 4D printing involves creating voxel units with distinct material properties that transform in response to stimuli; however, optimally distributing these properties to achieve specific target shapes remains a significant challenge. This study introduces an optimization method combining deep learning (DL) and an evolutionary algorithm, focusing on a solvent-responsive hydrogel as the target material. A sequence-enhanced parallel convolutional neural network is developed and generated a dataset through finite element simulations. This DL model enables high-precision prediction of hydrogel deformation. Furthermore, a progressive evolutionary algorithm (PEA) is proposed by integrating the DL model to construct a DL-PEA framework. This framework supports rapid reverse engineering of the desired shape, and the average design time for specified target shapes is reduced to ≈3.04 s. The present findings illustrate how 4D printing of optimized hydrogel designs can effectively transform in response to environmental stimuli. This work provides a new perspective on the application of hydrogels in 4D printing and presents an efficient tool for optimizing 4D-printed voxelized composite structures.

To solve the labor shortage, robots have dramatically changed the world by combining powerful deep learning (DL) technology. Certainly, DL technology has become the key point of the widespread robot application. Efficient DL models depend on high-quality datasets and optimized architectures. However, some open datasets contain anomalous data that degrade model performance. Moreover, complex structures and high computational costs limit the adoption of DL models. This study proposes a dataset purification-based lightweight DL model construction strategy to solve these challenges. Initially, a dataset purification method is developed to filter out the anomaly data in a newly created dataset, which utilizes a lightweight cross-scale DL model OGNet to detect the anomaly data to achieve dataset purification. Subsequently, a highly efficient lightweight OGNet-based object detection (OD) model family, YOLO-OG, is presented to train the purified dataset. To evaluate the proposal, the strategy is implemented on the Empty-dish Recycling Robot. Experiments show that OGNet achieves excellent accuracy with only 0.68 M parameters and 0.35 GFLOPs. On purification Dish-10 dataset, the mean Average Precision (mAP) of YOLO-OG increases a maximum of 4.28% than original Dish-10 dataset. Meanwhile, YOLO-OG outperforms other advanced OD models, achieving the best accuracy of 99.20% mAP and the smallest 1.60 M parameters. YOLO-OG also reaches 99.86% mAP on the Dish-20 open dataset. On three other open datasets, YOLO-OG also shows excellent performance and surpasses most of the other OD models, which confirms the strong generalization ability of YOLO-OG.